how to do research data analysis in excel

Data Analysis in Excel (A Comprehensive Guideline)

In this article, we will learn how to analyze data in Excel , including:

Different Excel functions, such as VLOOKUP , INDEX-MATCH , SUMIFS , CONCAT , and LEN functions.
Using Excel charts – learn how to create various chart types, customize them, and interpret the insights they offer, and how to apply conditional formatting effectively for data analysis purposes.
Creating pivot tables, performing calculations, and generating insightful reports.
Using Excel’s sorting and filtering capabilities.
The What-If Analysis feature in Excel and explore different scenarios by changing input values and observing the resulting outputs.
Implementing data validation techniques to maintain data accuracy.
The benefits of using tables and the built-in Analyze Data feature in Excel , which provides insights and recommendations based on your data.
Introducing the Analysis ToolPak add-in, which offers a wide range of statistical functions and tools, including descriptive analysis and ANOVA ( Analysis of Variance ).

Let’s use the following dataset as a demonstration of analyzing data in Excel.

Download Practice Workbook

Download the workbook and practice.

Analyze Data in Excel.xlsx

How to Analyze Data in Excel

Method 1 – use excel functions to analyze data.

Case 1.1 – The VLOOKUP Function

The VLOOKUP function is a frequently used function for looking up any particular data from a dataset. In the following example, we want to know how many goals an individual (for instance, Alex ) has scored.

The formula in cell F5 is

Here, Excel is looking for the value in cell E5 within the range B5:C14 and retrieving the corresponding value from the second column of that range.

Case 1.2 – INDEX and MATCH Functions

The formula in this case is:

Formula Breakdown

MATCH(E5, B5:B14, 0) → The MATCH function searches for the value in cell E5 within the range B5:B14 . The 0 as the third argument indicates an exact match. Output: 1

Case 1.3 – The SUMIFS Function

The SUMIFS function gets the sum of a range of cells with a set of conditions.

If you want to get the goals scored by the players from Group A and Group B separately, the formula you can use in cell G5 is:

The formula sums the values in the range $D$5:$D$14 but only includes values where the corresponding cells in the range $C$5:$C$14 match the value in cell F5 .

Case 1.4 – The CONCAT Function

Let’s join the first and last names of certain individuals here using the CONCAT function in Excel .

The formula in cell D5 is:

The formula joins the values in cells B5 and C5 , with a space between them, resulting in a single combined text string.

Case 1.5 – The LEN Function

You can count the number of characters of a cell or an array using the LEN function .

The formula in cell E5 is:

Method 2 – Data Analysis Using Excel Charts

Select the range F4:G6.
Go to the Insert tab and select any column chart .

Excel will create a column chart for you.

Method 3 – Apply Conditional Formatting to Analyze Data

Select the dataset in the range C5:C14.
Go to the Home tab and choose Conditional Formatting, then select a set of Data Bars .

Excel will add data bars.

Method 4 – A Pivot Table

Let’s calculate the number of goals scored by Group 1 and Group 2 players using the Pivot Table .

Select the dataset in range B4:B14.
Go to the Insert tab and select PivotTable .

A box will appear. We have chosen a New Worksheet as the destination of the Pivot Table .

Drag the fields in the areas ( Group in Rows and Goal in Values ) shown in the image.

Excel calculates the sum of goals.

Method 5 – Sorting Data in Excel

Suppose you want to sort the dataset in a descending order ( Largest to Smallest ).

Select the range C5:C14.
Go to the Data tab and select the Sort Z to A icon for descending order.

Select Expand the selection option from the warning window.

Your data will be sorted.

Method 6 – Filtering Data in Excel

Suppose you want to see the performance of the players of Group A .

Select range B4:D14 .
Go to the Data tab and activate the Filter option.

Filter your dataset from the drop-down icon in the column heading. We have selected Group A in the Group column.

Excel will get the list of all Group A players and their performance.

Method 7 – Excel What-If Analysis Feature

What-If Analysis in Excel refers to a set of tools and techniques that allow you to explore different scenarios and observe the potential impact on the results of your formulas or models. Excel provides several features for performing what-if analysis, including:

Data Tables: Data Tables allow you to create a table displaying multiple results based on input values. You can perform either one-variable or two-variable data tables to see how changing inputs affect the final results.
Goal Seek: Goal Seek helps you determine the input value needed to achieve a specific result. You specify a target value, and Excel automatically adjusts the input value until it reaches the desired outcome.
Scenario Manager: Scenario Manager enables you to create and compare different sets of input values for your model. You can define multiple scenarios with varying inputs and switch between them to see the impact on the calculated results.

We will show an example of the Goal Seek feature. Suppose you have 100 units of a product to be sold. You want to see the necessary unit price if you want to get a revenue of $200 .

The formula in C6 is:

This is very simple as we all know that the unit price will have to be $2 . However, the fun with this Goal Seek feature is that you do not have to manually put the unit price. Rather, Excel will find it for you.

Go to the Data tab and select What-If Analysis , then select Goal Seek .

You want the revenue ( To value ) to be $ 200 and get the unit price in cell C5 . So, the Set cell is C6 and the cell for By changing cell is C5 . Put those values in the dialog box and click OK .

Excel will put the unit price in C5 . Put the Revenue in the currency format if you want.
Modify the Units Sold value and repeat the process to see how it affects the result.

Read More: How to Perform Case Study Using Excel Data Analysis

Method 8 – Data Validation

Let’s get back to our previous example (from the VLOOKUP section). We want to select a player’s name from all the available options rather than manually typing their names.

Select cell E5.
Go to the Data tab and select the Data Validation option.

A Data Validation box will pop up. Choose List in the Allow field.
Set the source to =$B$5:$B$14 .

You can now select the names from the drop-down icon.

Once you select a name, you will get the number of goals the player scored.

Method 9 – Excel Table

Select the dataset in range D5:D14.
Press CTRL + T.

Excel will create a table.

Let’s see how you can get the total goals scored by these players without using any Excel Function .

Click on any cell of the table.
Go to the Table Design tab (this tab will be seen only if you select a cell of the table first).
Select Table Style Options and check the Total Row box.

Excel shows the total goals scored.

Read More: How to Analyse Qualitative Data from a Questionnaire in Excel

Method 10 – The Analyze Data Feature

Add this feature to your ribbon. Put the cursor on the Home ribbon and right-click, then select Customize the Ribbon .

Select New Group and set its position on the Home ribbon.
Select All Commands and add Analyze Data to this newly created group.

Go to the Home tab and select Analyze Data .

Excel-recommended options in Analyzing Data feature

Excel will recommend several options for data analysis.

Method 11 – Using the Analysis ToolPak Add-in

Go to the File tab and select Options . The Excel Options box will open.
Go to Add-ins and select Excel Add-ins in the Manage field, then click Go .

Check the box for Analysis ToolPak and click OK .

Let’s do some analysis using this add-in.

Read More: How to Convert Qualitative Data to Quantitative Data in Excel

Descriptive Analysis with the ToolPak

Select range C5:C14.
Go to the Data tab and select Data Analysis (This will be available once you activate the Analysis ToolPak add-in).

A Data Analysis box will pop up. Select the Descriptive Statistics option and click OK .

Set the input range and the output range and click OK . Check Summary statistics .

You will get the descriptive statistics of the selected input range in your Excel workbook.

Read More: How to Make Histogram Using Analysis ToolPak

ANOVA Analysis in Excel with ToolPak

ANOVA stands for Analysis of Variance . It is a statistical method used to compare the means of two or more groups to determine if there are any significant differences between them.

Go to the Data tab and select Data Analysis .
Select ANOVA from the Data Analysis box and click on OK .

Set the input and output ranges.

Excel will perform the analysis for you.

Read More: How to Analyze Data in Excel Using Pivot Tables

Things to Remember

Data Validation ensures accuracy.
The INDEX-MATCH function is better than the VLOOKUP function.
You need to refresh the Pivot Table when you change your dataset.

Frequently Asked Questions

1. What are the advantages of using the Analyze Data feature in Excel over manual analysis techniques?

Advantages of using the Analyze Data feature in Excel over manual analysis techniques include saving time by automating tasks, an easy-to-use interface, lots of helpful tools and functions, the ability to customize, and working well with other Excel features.

2. What is the difference between descriptive and inferential statistics?

Descriptive statistics help describe data by summarizing it while inferential statistics help make predictions about a larger group based on a smaller sample.

3. What are the uses of ANOVA?

ANOVA is used to compare the averages of different groups, see how categorical variables affect outcomes, analyze experiments, and understand different sources of variation in data.

Analyze Data in Excel: Knowledge Hub

How to Install Data Analysis in Excel
How to Use Data Analysis Toolpak in Excel
How to Enter Data for Analysis in Excel
How to Use Analyze Data in Excel
[Fixed!] Data Analysis Not Showing in Excel
How to Analyze Raw Data in Excel
How to Analyze Large Data Sets in Excel
How to Analyze Text Data in Excel
How to Analyze Time Series Data in Excel
How to Analyze Sales Data in Excel
How to Analyze Likert Scale Data in Excel
How to Analyze qPCR Data in Excel
How to Analyze Quantitative Data in Excel
How to Analyze Qualitative Data in Excel
Organize Data in Excel: A Complete Guide
Rearranging in Excel
How to Add Tags in Excel?
How to Summarize Data in Excel

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AKIB BIN RASHID, a materials and metallurgical engineer, is passionate about delving into Excel and VBA programming. To him, programming is a valuable time-saving tool for managing data, files, and internet-related tasks. Proficient in MS Office, AutoCAD, Excel, and VBA, he goes beyond the fundamentals. Holding a B.Sc in Materials and Metallurgical Engineering from Bangladesh University of Engineering and Technology, MD AKIB has transitioned into a content development role. Specializing in creating technical content centred around Excel and... Read Full Bio

10 Essential Excel Features For Data Analysts (and How to Use Them)

Spreadsheets are still in style! The use of electronic spreadsheets date back to 1979 and are still one of the most popular ways to review and manipulate data.

Today, Microsoft’s ubiquitous spreadsheet program Excel has over 750 million users and is used in some of the largest companies worldwide. I personally use Excel almost every day because it can sometimes lead to quicker results than spinning up Python or loading the data into a database.

One of the reasons Excel is so popular is because it is jam-packed with features and functions that can be used to clean , aggregate, pivot, and graph data. In this article, we’ll go over the 10 features and functions for using data analysis in Excel I think every analyst needs to know:

Pivot tables and pivot charts
Conditional formatting
Remove duplicates
DAYS and NETWORKDAYS

You can click any of the features to skip ahead to them.

Before we get started…

In order to show the power of data analysis in Excel, we need some data to play around with and graph. I am using the Customer Personality Analysis dataset from Kaggle in these examples. You can download it to follow along.

… and, if you want to watch along…

Here’s our very own senior data scientist, Tom, explaining these functions in a video:

1. Pivot tables and pivot charts

One of my favorite features in Excel is pivot charts and, as a close follow-up feature, pivot tables. Pivot charts visualize the data expressed in a pivot table, giving us insight at a glance. Pivot tables provide a simple approach to reformatting columns and rows, transforming them into groupings, statistics, or summaries. We can create a chart along with the table using the PivotChart feature under Insert .

Let’s say we wanted to count the records grouped by Marital_Status . Using a pivot table makes that super simple, because it transforms the data and aggregates it for us.

To create a pivot chart and pivot table, first select the range of data you want to include then click Insert > PivotChart > PivotChart & PivotTable . The Create PivotTable editor will pop up.

The selected range will appear in the Table/Range field by default. Click OK and the pivot table will generate.

In the PivotChart Fields, drag Marital_Status from the field list into the Axis (Categories) box. Then drag Marital_Status from the field list into the Values box. Since the Marital_Status data is a string, the Values aggregation should default to Count . If the data type were numeric, the aggregation defaults to Sum .

The pivot table and chart should populate. You can add additional dimensions or filters by dragging new fields into the corresponding box. It only takes a few clicks to aggregate data and visualize it in Excel, which is why it is such a widely-used tool.

2. Conditional formatting

Thinking about it, I probably use conditional formatting more than any other feature in Excel. Conditional formatting allows you to highlight or hide cells based on a rule you specify. Apply the rules to one cell or multiple cells in the same worksheet. It is useful for highlighting outliers , duplicates, or patterns in data.

Let’s say we want to highlight all Year_Birth values greater than 1987 in the dataset.

Select the Year_Birth column and click Conditional Formatting > Highlight Cells Rules > Greater Than . The editor will pop up:

Enter 1987 and click OK . The cells in the column with a value greater than 1987 will turn light red.

If we decide we want to adjust the conditional formatting rule we just created, follow path Conditional Formatting > Conditional Formatting Rules Manager .

From the manager we are able to create new rules or edit existing ones. It is possible to have multiple rules affecting the spreadsheet.

3. Remove duplicates

Data is often messy, so it is important that you know how to remove duplicates. Using conditional formatting rules, you can highlight the duplicate data to review it before deleting it. The Remove Duplicates feature is available under Data > Data Tools > Remove Duplicates .

Highlight the dataset in Excel and click Remove Duplicates . The Remove Duplicates editor appears:

The editor allows us to select columns that should be included when deleting the duplicates. Make sure the My data has headers checkbox is marked if the column names are not displaying.

Click OK . The duplicates will be dropped from the dataset. It will tell us how many unique values remain.

XLOOKUP is like a combination of VLOOKUP and HLOOKUP, since it can go either vertically or horizontally to lookup a value from a range. It essentially allows you to use a selected range as a lookup table and return a “looked up” result to a cell. The syntax is as follows:

=XLOOKUP(lookup_value, lookup_array, return_array, [if_not_found], [match_mode], [search_mode])

Let’s say we want to look up the Year_Birth based on an entered ID value. In cell AD2 , enter an ID value—for example, 8755. In cell AE2 , enter the XLOOKUP formula:

The lookup_value is the value we want looked up by the function, so we enter AD2 .

The lookup_array is a column or row that contains the lookup value, so we enter A2:A2241 since that will give us an array of IDs.

The return_array is the column or row that contains the value we want to return, so we select B2:B2241 since that will give us Year_Birth values.

The completed formula will look like this: =XLOOKUP(AD2, A2:A2241, B2:B2241)

Enter in different IDs and the corresponding Year_Birth will return.

Lookup functions are very powerful, and you can even join data from different sheets or nest lookup functions within each other. For example, you could sum the value of multiple lookups.

The IFERROR function is used to create a custom error message when a formula results in an error. For example, we can use it to wrap our XLOOKUP function so it returns a clear message if the ID isn’t found. The syntax is relatively simple.

=IFERROR(value, value_if_error)

Going back to the XLOOKUP function, if we enter an ID in to AD2 that doesn’t exist in the lookup array, AE2 outputs #N/A . Instead, let’s return “ID Not Found.” For the value in the IFERROR function, use the XLOOKUP function. It should look like this:

=IFERROR(XLOOKUP(AD2, A2:A2241, B2:B2241), “ID Not Found”)

In addition to text, you can point the value_if_error at another cell too. If you target a blank cell as the value for value_if_error , 0 will appear in the cell.

Similar to the lookup functions, MATCH can be used when you need the position of a value in a range instead of the value itself.

This is the syntax for MATCH:

=MATCH(lookup_value, lookup_array, [match_type])

When writing the function, it is important to know which match type to use. Although it is optional and defaults to 1, the available options are -1, 0, or 1.

-1: Finds the smallest value that is greater than or equal to lookup_value .
0: Finds the first value that is exactly equal to lookup_value .
1: Finds the largest value that is less than or equal to lookup_value .

If we want to find the first time the looked up birth year occurs, add a the following MATCH formula to cell AF2 :

=MATCH(AE2,B:B, 0)

7. COUNTBLANK

The COUNTBLANK function is fairly intuitive, but it is an important function for data wrangling in analytics because many machine learning algorithms are sensitive to nulls. By knowing how many values are null, you have a better understanding of how to approach them. For example, if a lot of values are null you should drop the column. If few values are null you should impute a value to fill the null. COUNTBLANK counts the number of empty cells in a range. The syntax is simple:

=COUNTBLANK(range)

We want to count the number of nulls in the Income column, so add this formula to cell AG2 :

=COUNTBLANK(E2:E2241)

8. DAYS and NETWORK DAYS

DAYS and NETWORKDAYS are separate functions, but they are similar enough to not warrant individual entries in my list. The DAYS function simply returns the number of days between two dates, whereas the NETWORKDAYS function is slightly different in that it excludes weekends and specified holidays. It only returns the number of working days between two dates. The syntax for both is easy to remember:

=DAYS(start_date, end_date)

=NETWORKDAYS(start_date, end_date, [holidays])

At my job, one of the things I analyze is usage data. I use these functions for things like counting the number of days since a user has logged in or used a tool. Since our software is used in schools, sometimes it makes sense to exclude weekends and holidays from our usage data so I’m thankful for the NETWORKDAYS function.

The RANK function orders a number by its size relative to other values in a list and returns the desired rank. That means the rank of the number would be its position if the list becomes sorted by ascending or descending order. For example, sort Income in descending order so the biggest value is at the top and that could be rank 1. RANK gives duplicate numbers the same rank, but cumulatively counts. That means if two values are rank four, the next rank will be six, not five (1,2,3,4,4,6). The syntax for RANK takes 3 arguments.

=RANK(number, ref, [order])

It is important to note that [order] can be set to 0 for descending and 1 (or greater) for ascending.

We want to rank the income (column E) of our customers, so we will use the following formula in cell AH2 :

=RANK(E2, E2:E2241, 0)

In the bottom right corner of cell AH2 , click the square and drag it down to the last row of data and the formula will automatically copy allowing us to quickly generate a rank for each income value.

10. SUMPRODUCT

The last function on my top 10 list is SUMPRODUCT . It is great when you need to do arithmetic on specific groups of values. It can be used to add, subtract, multiply or divide the selected numerical values for corresponding ranges. Although it sounds complicated, the logic is fairly intuitive once you try it. The syntax, however, is not very intuitive.

=SUMPRODUCT(array1, [array2], [array3], …)

We want to sum the KidHome for all rows where Education equals Graduation (C2) and Marital_Status equals Single (D2) . We will add this formula to cell AI:

=SUMPRODUCT((C2:C2241=C2)*(D2:D2241=D2)*(F2:F2241))

If we wanted to see a different Education value, PhD for example, we could change C2 to C6 in the formula.

Summary and next steps

This is by no means an exhaustive list of every feature and function Excel offers, but these 10 functions help me analyze and clean data without the hassle of booting up Python or loading the data in a SQL server.

Pivot charts are great for small data sets, especially if you need to share the data with non-technical people. Even though Excel seems like a cumbersome tool in a world of big data, it remains one of the most popular data analytics tools on the planet for a reason.

But don’t take our word for it— CareerFoundry Data Analytics Program graduate Nick had this advice for other potential career-changers, after successfully completing his move from math teacher to data analyst:

To know Excel—no matter what your role—is important. If you work at a business and you know some of the more intermediate-to-advanced Excel skills, it’s going to help save you time, and maybe even come up with something that’s going to impress your boss. Excel is a huge tool that is a lot more robust than I knew it to be.

If you’d like to learn more about data analytics, try out this free 5-day short course , or check out some of our other data analytics articles:

SQL Cheatsheet: Learn Your First 8 Commands
How to Create a Checkbox in Excel: A Step-by-Step Guide
What Are The Key Skills Every Data Analyst Needs?

How to Do Data Analysis in Excel: A Comprehensive Step-by-Step Guide

How to Do Data Analysis in Excel

Data analysis in Excel is like fitting the pieces of a puzzle together. You start by importing your data, then use built-in tools to clean, organize, and finally analyze it. You’ll create charts and pivot tables to make sense of numbers, and use functions to extract meaningful insights. Follow these steps and you’ll be a data wizard in no time.

Step-by-Step Tutorial on How to Do Data Analysis in Excel

Let’s dive into how to analyze data using Excel. This guide will help you from importing the data to visualizing insights with charts and pivot tables.

Step 1: Import Your Data

First, import your data into Excel.

To import data, go to the "Data" tab and choose the source, such as a CSV file or an external database. Navigate to your file, and Excel will load the data into a new worksheet.

Step 2: Clean Your Data

Next, clean your data to ensure accuracy.

Clean data by removing duplicates, fixing errors, and filling in missing values. Use features like "Remove Duplicates" under the "Data" tab and "Find & Select" to pinpoint and fix issues.

Step 3: Organize Your Data

Then, organize your data for analysis.

You can sort and filter data using the options in the "Data" tab. Sorting helps you arrange the data in a specific order, while filtering lets you focus on subsets of data that meet certain criteria.

Step 4: Create Pivot Tables

Now, create pivot tables to summarize your data.

Go to "Insert" and select "PivotTable." Choose your data range and where you want the PivotTable to appear. Drag and drop fields into the appropriate areas to create meaningful summaries.

Step 5: Use Functions for Analysis

Apply Excel functions to analyze your data.

Functions like SUM, AVERAGE, and VLOOKUP can be found under the "Formulas" tab. These functions help you perform calculations and look up information within your data set.

Step 6: Visualize Data with Charts

Finally, visualize your data with charts.

Select your data and go to "Insert" to choose from various chart types like bar, line, or pie charts. Charts help you see trends and patterns that are not obvious in raw data.

After completing these actions, your data will be organized, analyzed, and visualized, giving you valuable insights to make informed decisions.

Tips for Data Analysis in Excel

Use keyboard shortcuts like Ctrl+C for copy and Ctrl+V for paste to save time.
Always double-check your data for errors before starting your analysis.
Use conditional formatting to highlight key data points.
Leverage Excel’s "Quick Analysis" tool by selecting your data and pressing Ctrl+Q.
Save your work frequently to avoid losing data.

Frequently Asked Questions

How do i remove duplicates in excel.

You can remove duplicates by selecting your data, going to the "Data" tab, and clicking "Remove Duplicates."

How can I create a PivotTable?

Select your data range, go to the "Insert" tab, and choose "PivotTable." Drag and drop fields into the PivotTable layout.

What is the VLOOKUP function?

VLOOKUP is a function that looks for a value in the first column of a table and returns a value in the same row from another column.

How do I use conditional formatting?

Select your data, go to the "Home" tab, and choose "Conditional Formatting" to apply rules that format cells based on their values.

How can I save my data analysis work?

You can save your work by pressing Ctrl+S or going to the "File" tab and selecting "Save."

Import Your Data
Clean Your Data
Organize Your Data
Create Pivot Tables
Use Functions for Analysis
Visualize Data with Charts

Data analysis in Excel doesn’t have to be a daunting task. With the right steps and a bit of practice, you can transform raw data into meaningful insights in no time. From importing and cleaning your data to using powerful tools like PivotTables and charts, Excel offers everything you need to become a data analysis pro.

Remember, mastering data analysis in Excel is a gradual process. The more you use these features, the more intuitive they become. So don’t hesitate—start exploring the wealth of tools Excel offers and watch your data analysis skills soar. Whether you’re a student, a professional, or just someone looking to improve your Excel game, these steps will set you on the path to success. Happy analyzing!

Matt Jacobs has been working as an IT consultant for small businesses since receiving his Master’s degree in 2003. While he still does some consulting work, his primary focus now is on creating technology support content for SupportYourTech.com.

His work can be found on many websites and focuses on topics such as Microsoft Office, Apple devices, Android devices, Photoshop, and more.

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Data Analytics

Caltech Bootcamp / Blog / /

Tutorial: Data Analysis in Excel

Written by John Terra
Updated on February 23, 2024

Our world today is awash in the rising tide of data. We use data analysis to deal with the daily flood of big data, hoping to make sense of it and turn it into actionable insights. Microsoft Excel, a venerable presence in our digital world for years, is one of the most popular data analysis applications. Excel is an all-in-one data management application that lets you easily import, explore, analyze, clean and visualize your data.

This article discusses data analysis in Excel, especially the different data analysis methods available in the application. We will explore conditional formatting, pivot tables, ToolPak and more. We’’ also share a way to get practical experience working with this powerful tool through online data analytics training .

So, let’s get started.

How to Use Data Analysis in Excel

Charts are a great way to present a narrative with graphics. Charts summarize data so data sets are easier to understand and analyze. Since Excel is well known for its ability to organize and compute numbers and a chart is a graphical depiction of a set of facts, it’s easy to see why this application fits so well.

Charts are visual depictions of data using symbols like bars in a Bar Chart or lines in a Line Chart to represent data. Excel provides users with various chart types to pick, or they can select the Excel Recommended Charts option and examine charts tailored to their data.

Excel charts are ideal for helping with data analysis by emphasizing one or several of a report’s components. Analysts can use Excel charts to filter out unnecessary “noise” from the story they’re trying to convey and instead focus on the most vital parts of the data. Analysts can quickly create columns, pie charts, lines or bar charts by navigating to the Insert tab and choosing the Charts command group.

Here’s how to create these charts:

Choose a data range
Select Insert > (pick desired chart type from icons)
Modify the inserted chart as needed

And now, let’s continue our exploration of data analysis in Excel by looking at different methods you can use.

Also Read: Data Analytics Applications: Types, Use Cases, and Top Tools

The Various Methods for Data Analysis in Excel

Find/search.

=FIND/=SEARCH is suitable for locating specific text inside a data source. Both commands are mentioned here because =FIND yields a case-sensitive match (for example, if you query “Big,” you will only get Big=true results), while a =SEARCH for “Big” matches with Big or big, expanding the query. This is very useful when searching for abnormalities and unique identifiers.

=FIND(TEXT,WITHIN TEXT,[START NUMBER]) Otherwise, =SEARCH(TEXT,WITHIN TEXT,[START NUMBER])

CONCATENATE

CONCATENATE is one of the most straightforward yet powerful formulas for data analysis. Text, numbers, dates, and other data from many different cells can be combined into one. This is an excellent method for generating product SKUs, Java queries and API endpoints.

=CONCATENATE(SELECT the cells you would like to merge)

=COUNTA determines if a cell is empty. Data analysts often encounter incomplete data sets. COUNTA allows them to examine gaps in the dataset without restructuring it.

=COUNTA(SELECT CELL)

COUNTIF is a commonly used Excel function that counts cells in a range that satisfies a single condition.

=COUNTIF (range, criteria)

=LEN quickly returns the number of characters in each cell. The =LEN formula can be used to decide the number of characters in a cell, distinguishing two kinds of product Stock Keeping Units (SKUs). LEN is particularly important when determining between Unique Identifiers (UIDs) since these are sometimes long and must be in the proper sequence.

=LEN(SELECT CELL)

The SUMIF function gives the sum of the cells that fulfill a single condition.

=SUMIF (range, criteria, [sum_range])

This fantastic, versatile function eliminates all spaces from a cell except for single spaces between words. TRIM is often used to eradicate trailing spaces, usually when the material is copied from a different source, or users enter spaces at the end of the text.

=TRIM(piece of text)

AVERAGEIFS, like SUMIFS, lets you take an average based on one or more parameters.

=AVERAGEIF(SELECT CELL, CRITERIA, AVERAGE RANGE)

How to Perform Conditional Formatting

Conditional formatting helps highlight patterns and trends in your data, and you can create rules that define the cell formats on their values. Conditional formatting can be applied to an Excel table, a range of cells that is either a selection or a named range, or even a PivotTable report in Excel for Windows.

Just follow the steps below to perform conditional formatting.

If you want to change values in individual cells, you can do so. Select the Highlight Cells Rules or Top/Bottom Rules, then choose the option corresponding to your needs.
The color scale shows the cell’s color intensity, which corresponds to the value’s placement at the top or bottom of the range and emphasizes the relationship between the values in a cell range. Point to Color Scales, then click the desired scale.
If you want to emphasize the relationship of values within a cell range, point to Data Bars, then click the desired fill, creating a colored band across the cell.
If you want to highlight a cell range with three to five sets of values, each having its threshold, point to Icon Sets, then click a set. For example, you could use three icons to emphasize cells indicating sales of less than $90,000, $50,000, and $30,000. Alternatively, you can assign a 5-point rating system to autos and then use five icons.

Also Read: Data Analytics in Business: A Complete Overview

Types of Data Analysis in Excel

Let’s look at a sample of how Microsoft Excel handles different types of data analysis.

Sorting data is an essential part of data analysis. You can sort Excel data by multiple or a single column in ascending or descending order. When sorting data in a spreadsheet, you can rapidly rearrange the data to discover values. It’s possible to sort a range or table of data on one or more columns of data. For example, you can rank personnel by department and last name.

Data analysts use filtering when they want to get data that will match specific conditions. You may use the FILTER function to filter data sets that depend on your provided criteria. This filter feature is currently only available to Microsoft 365 users.

Conditional Formatting

Conditional formatting lets you highlight cells with a specific color based on the cell’s value.

A simple Excel graphic can convey more information than a statistics page, and it’s easy to make charts in Excel.

Datasets are collections of contiguous cells in an Excel worksheet that contain data to be analyzed. If you want to make the software plugin Analyse-it work with your data, follow a few simple guidelines when structuring the data on your Excel worksheet:

Your title should adequately describe the data. The dataset name defaults to its cell range if you don’t supply a title.
Use header rows with configurable labels. Each variable needs a distinct name. Measurements can be incorporated into a label by placing them in brackets after the name.
Rows carry information for each instance, and Excel only limits the number of rows.
Columns carry data for each variable.

Pivot Tables

Pivot tables are considered Excel’s most powerful and purposeful feature. Data analysts use them to summarize data stored in a table. The table organizes and rearranges statistics (or “pivot”) to highlight vital and valuable facts. Pivot tables can take an extensive data set and display the needed relevant data in a crisp, easy and manageable way.

Explaining the What-If Analysis with Solver

What-If Analysis changes values to try different values (or scenarios) for formulas. You may use different sets of values in one or multiple formulas to investigate all the possible results.

A solver is an add-in program for Excel that’s helpful on many levels and ideal for what-if analysis. You can use it to locate an optimal (either a maximum or minimum) value for a formula in a single cell, known as the objective cell. This process is subject to certain limits or constraints on the values of other formula cells.

Solver works with groups of cells called decision variables or just variable cells and is used to compute the formulas in objective and constraint cells. Solver also changes the decision variable cells’ values to work on the constraint cells’ limits.

Also Read: What is Exploratory Data Analysis?

The Data Analysis ToolPak

Here’s how to use the data analysis ToolPak:

Click the File tab, click Options, then click the Add-Ins category
Select Analysis ToolPak, then click the Go button
Check Analysis ToolPak, then click OK
Finally, click on Data Analysis on the Data tab in the Analysis group, and you’re on your way!

Data Analysis in Excel: Descriptive Statistics

Descriptive statistics are a data set’s most basic, fundamental ‘must know’ information. It gives you insights on:

Mean, median, mode, and range
Variance and standard deviation.

To generate a descriptive analysis, follow these steps:

Go to the Data tab > Analysis group > Data analysis
Select Descriptive Statistics, then click OK
Select your input range
Select the range from where you’d like to display the output
Check summary statistics
Your descriptive statistics are ready!

There are still so many functions, types, charts, and methods of using Excel in data analysis that we haven’t touched on, which speaks of its sheer versatility. For a tool that’s been around since 1985, it still has a prominent place in the science of data analysis!

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You might also like to read:

Data Analyst Job Description: What Aspiring Professionals Need to Know

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Data Analytics Certifications: Top Options in 2024

Best Data Analytics Tools in 2024 and Beyond

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Excel Roadmap for Data Analysis

Anantikabisht

Are you looking to excel in data analysis using Microsoft Excel?

You’re in the right place! In this article, we’ll provide you with a comprehensive roadmap for mastering data analysis with Excel. We’ll also guide you to valuable resources, YouTube channels, and free certificates to help you on your journey.

The Versatility of Excel

Excel is not just a spreadsheet program; it’s a versatile tool for data analysis, reporting, and visualization. Mastering Excel can open doors to various career opportunities in data-driven fields.

Chapter 1: Getting Started

1.1 navigating excel.

Understand Excel’s interface, including ribbons, worksheets, and cells.
Customize Excel settings to optimize your workflow.

1.2 Data Entry and Formatting

Learn to input and format data efficiently.
Explore formatting options like fonts, cell styles, and conditional formatting.

1.3 Basic Formulas and Functions

Grasp the fundamentals of Excel formulas and functions, including SUM, AVERAGE, and COUNT.
Understand cell references (relative, absolute, and mixed).

Chapter 2: Data Manipulation

2.1 sorting and filtering.

Organize data by sorting columns.
Use filtering to focus on specific data subsets.

2.2 Data Tables and Lists

Convert data into Excel tables for easier management.
Explore data validation to ensure data accuracy.

2.3 Data Cleaning and Transformation

Handle common data cleaning tasks like removing duplicates and handling missing values.
Perform data transformations using functions like CONCATENATE and TEXT.

Chapter 3: Advanced Data Analysis

3.1 pivottables.

Master PivotTables for summarizing and analyzing large datasets.
Learn to group, filter, and create calculated fields in PivotTables.

3.2 Formulas for Advanced Analysis

Explore advanced formulas like VLOOKUP, HLOOKUP, INDEX, and MATCH.
Combine formulas to solve complex data problems.

3.3 Data Analysis Add-Ins

Use Excel’s built-in data analysis tools, including Solver and Goal Seek, for optimization and scenario analysis.

Chapter 4: Data Visualization

4.1 charts and graphs.

Create various types of charts, including bar charts, line graphs, and pie charts.
Customize charts to convey insights effectively.

4.2 Sparklines and Conditional Formatting

Use sparklines to create mini-charts within cells.
Apply conditional formatting to highlight important data trends.

4.3 Interactive Dashboards

Build interactive dashboards using Excel’s features and functions.
Create dynamic reports that update with new data.

Chapter 5: Advanced Techniques

5.1 macros and automation.

Record and run macros to automate repetitive tasks.
Learn to write VBA (Visual Basic for Applications) code to extend Excel’s functionality.

5.2 Data Validation and Protection

Implement data validation rules to control data input.
Protect worksheets and workbooks with passwords.

5.3 What-If Analysis

Explore scenario analysis and sensitivity analysis using data tables.

Use Goal Seek to find desired outcomes by varying input values

Chapter 6: Collaboration and Sharing

6.1 sharing workbooks.

Collaborate with others by sharing workbooks and tracking changes.
Understand version control and resolving conflicts.

6.2 Excel Online and Cloud Integration

Explore Excel Online for web-based collaboration.
Integrate Excel with cloud services like OneDrive and SharePoint.

Chapter 7: Continuing Education and Practice

Data analysis is a continuous learning journey.

7.1 Online Courses and Tutorials

Enroll in online courses on platforms like Coursera, edX, or LinkedIn Learning.
Follow Excel tutorials on YouTube channels dedicated to data analysis.

7.2 Practical Application

Apply Excel skills to real-world projects.
Analyze datasets, create reports, and solve data-related challenges.

7.3 Building a Portfolio

Showcase your Excel projects in a portfolio.
Highlight your data analysis and visualization expertise to potential employers.

7.4 Follow Blogs and Forums

Data analysis tools and techniques evolve. Stay up-to-date with industry trends.

Stay informed by reading data analysis blogs and participating in forums like Stack Overflow.

Chapter 8: Certifications

Consider earning certifications to validate your Excel data analysis skills.

8.1 Microsoft Excel Certifications

Microsoft offers various certification levels, including Microsoft Certified: Excel Associate and Excel Expert.

8.2 Simplilearn Certifications

https://simpli-web.app.link/e/cH5JVnCXCjb

8.3 Great Learning Certification

https://www.mygreatlearning.com/academy?referrer_code=GLTF2A1ZW_OOQ

8.4 Coursera Certifications

https://www.coursera.org/courses?query=free

8.5 Edx Certifications

https://www.edx.org/learn/data-analysis

Chapter 9: YouTube Channels for Learning

YouTube is a treasure trove of Excel tutorials and data analysis tips. Here are some channels to follow:

9.1 ExcelIsFun

Offers a wide range of Excel tutorials, from beginner to advanced topics.

https://youtube.com/@excelisfun?si=4IfEsbBQS5iP8kTE

9.2 MyExcelOnline

Focuses on advanced Excel techniques and data analysis.

https://youtube.com/@MyExcelOnline?si=bcRH6VqlDwznHvzf

9.3 Chandoo

· Teaches data visualization techniques and Excel dashboards and indepth.

https://youtube.com/@chandoo_?si=1_ZfQmGLQKS4S7tR

9.4 Excel on Fire

Explores creative ways to visualize data using Excel.

https://youtube.com/@OzduSoleilDATA?si=EWcnbJJJa7qgX9Sv

9.5 Alex The Analyst

· He has his full playlist dedicated to Excel

https://youtube.com/playlist?list=PLUaB-1hjhk8Hyd5NiPQ9CND82vNodlFF5&si=opkDfJYnQi8-Czrb

9.6 Kenji Explains

· His whole channel is dedicated to Excel only Excel in various fields.

https://youtube.com/@KenjiExplains?si=4cMQmsY-y_dOWNer

Q1: How long does it take to become proficient in data analysis using Excel?

A1 : The time required to become proficient varies based on your starting point and the depth of knowledge you aim to achieve. Basic proficiency can be acquired in a few weeks, while becoming an advanced Excel data analyst may take several months or more of continuous learning and practice.

Q2: Are there any recommended online courses for learning data analysis with Excel?

A2: Yes, there are numerous online courses and tutorials available on platforms like LinkedIn Learning, Coursera, Udemy, and Microsoft’s official Excel training resources. These courses cater to various skill levels, from beginner to advanced.

Q3: Can I use Excel on both Windows and Mac?

A3 : Yes, Excel is available for both Windows and Mac operating systems. While the core functionality remains consistent, there may be slight differences in the user interface and features between the two versions

Q4: What is the importance of Excel in data analysis?

A4: Excel is vital for data analysis as it allows you to organize, manipulate, and visualize data effectively.

Q5: Are these resources and certificates really free?

A5 : Yes, the mentioned resources and certificates offer free access to their content, but some may also provide paid certification options.

By following this Excel data analysis roadmap and leveraging the recommended resources and certificates, you’ll be well on your way to becoming a proficient data analyst. Excel’s capabilities are vast, and mastering them can significantly enhance your analytical skills and career prospects.

So, start your journey today and unlock the potential of data analysis with Excel! 📊💼

Written by Anantikabisht

Data Wizard Venturing into AI | Data Scientist with a Focus on AI | Freelancing for Innovative AI Challenges

Text to speech

#1 Excel tutorial on the net

R Square | Significance F and P-Values | Coefficients | Residuals

This example teaches you how to run a linear regression analysis in Excel and how to interpret the Summary Output.

Below you can find our data. The big question is: is there a relation between Quantity Sold (Output) and Price and Advertising (Input). In other words: can we predict Quantity Sold if we know Price and Advertising?

1. On the Data tab, in the Analysis group, click Data Analysis.

Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in .

2. Select Regression and click OK.

3. Select the Y Range (A1:A8). This is the predictor variable (also called dependent variable).

4. Select the X Range(B1:C8). These are the explanatory variables (also called independent variables). These columns must be adjacent to each other.

5. Check Labels.

6. Click in the Output Range box and select cell A11.

7. Check Residuals.

8. Click OK.

Excel produces the following Summary Output (rounded to 3 decimal places).

R Square equals 0.962 , which is a very good fit. 96% of the variation in Quantity Sold is explained by the independent variables Price and Advertising. The closer to 1, the better the regression line (read on) fits the data.

Significance F and P-values

To check if your results are reliable (statistically significant), look at Significance F ( 0.001 ). If this value is less than 0.05, you're OK. If Significance F is greater than 0.05, it's probably better to stop using this set of independent variables. Delete a variable with a high P-value (greater than 0.05) and rerun the regression until Significance F drops below 0.05.

Most or all P-values should be below below 0.05. In our example this is the case. ( 0.000 , 0.001 and 0.005 ).

Coefficients

The regression line is: y = Quantity Sold = 8536.214 -835.722 * Price + 0.592 * Advertising. In other words, for each unit increase in price, Quantity Sold decreases with 835.722 units. For each unit increase in Advertising, Quantity Sold increases with 0.592 units. This is valuable information.

You can also use these coefficients to do a forecast. For example, if price equals $4 and Advertising equals $3000, you might be able to achieve a Quantity Sold of 8536.214 -835.722 * 4 + 0.592 * 3000 = 6970.

The residuals show you how far away the actual data points are fom the predicted data points (using the equation). For example, the first data point equals 8500. Using the equation, the predicted data point equals 8536.214 -835.722 * 2 + 0.592 * 2800 = 8523.009, giving a residual of 8500 - 8523.009 = -23.009 .

You can also create a scatter plot of these residuals.

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Statistics By Jim

Making statistics intuitive

Descriptive Statistics in Excel

By Jim Frost 38 Comments

Descriptive statistics summarize your dataset, painting a picture of its properties. These properties include various central tendency and variability measures, distribution properties, outlier detection, and other information. Unlike inferential statistics , descriptive statistics only describe your dataset’s characteristics and do not attempt to generalize from a sample to a population .

In this post, I provide step-by-step instructions for using Excel to calculate descriptive statistics for your data. Importantly, I also show you how to interpret the results, determine which statistics are most applicable to your data, and help you navigate some of the lesser-known values.

Additionally, I include links to resources I’ve written that present clear explanations of relevant statistical concepts that you won’t find in Excel’s documentation. And, I use an example dataset for us to work through and interpret together!

Before proceeding, ensure that Excel’s Data Analysis ToolPak is installed. On the Data tab, look for Data Analysis , as shown below.

If you don’t see Data Analysis, install that ToolPak. Learn how to install it in my post about using Excel to perform t-tests . It’s free!

Let’s start with a caveat. Use descriptive statistics together with graphs. The statistical output contains numbers that describe the properties of your data. While they provide useful information, charts are often more intuitive. The best practice is to use graphs and statistical output together to maximize your understanding. At the end of this post, I display the histograms for the variables in this dataset.

For this example, we’ll assess two variables, the height and weight of preteen girls. I collected these data during a real experiment. To use this feature in Excel, arrange your data in columns or rows. I have my data in columns, as shown in the snippet below.

Displays portion of the dataset for this descriptive statistics example.

Download the Excel file that contains the data for this example: HeightWeight .

In Excel, click Data Analysis on the Data tab, as shown above. In the Data Analysis popup, choose Descriptive Statistics , and then follow the steps below.

Excel's Descriptive Statistics option in the Data Analysis menu.

Step-by-Step Instructions for Filling in Excel’s Descriptive Statistics Box

Under Input Range , select the range for the variables that you want to analyze. You can include multiple variables as long as they form a contiguous block. While you can explore more than one variable, the analysis assesses each variable in a univariate manner (i.e., no correlation ).
In Grouped By , choose how your variables are organized. I always include one variable per column as this format is standard across software. Alternatively, you can include one variable per row.
Check the Labels in first row checkbox if you have meaningful variable names in row 1. This option makes the output easier to interpret.
In Output options , choose where you want Excel to display the results.
Check the Summary statistics box to display most of the descriptive statistics (central tendency, dispersion, distribution properties, sum, and count).
Check the Confidence Level for Mean box to display a confidence interval for the mean. Enter the confidence level. 95% is usually a good value. For more information about confidence levels, read my post about confidence intervals .
Check Kth Largest and Kth Smallest to display a high and low value. If you enter 1, Excel displays the highest and lowest values. If you enter 2, it shows the 2 nd highest and lowest values. Etc.

For our example dataset, fill in the dialog box as shown below.

Excel's dialog box for descriptive statistics.

Interpreting Excel’s Descriptive Statistics Results

After Excel creates the statistical output, I autofit the columns for clarity.

As you can see, we’re assessing two variables, height in meters and weight in kilograms.

Generally, we’ll work our way down from the top of Excel’s descriptive statistics output. However, I’ll group the results into categories that make sense. Consequently, the following discussion doesn’t strictly follow the order of the output. If you want to learn more about the statistics, be sure to click the links for more detailed information!

Central Tendencies (Mean, Median, Mode)

A measure of central tendency describes where most of the values in the dataset occur. It’s the center of the distribution of values. Excel presents three measures of central tendency. Which one is best for your data?

Mean : This measure is the one with which you’re most familiar. It’s the sum of all observations divided by the number of observations. It’s best for data that follow symmetric distributions.
Median : This value splits your data in half. Half the values fall above the median while half are below it. It’s best for skewed distributions.
Mode : This measure represents the value that occurs most frequently in your data. It’s best for categorical and ordinal data.

The example data are continuous variables . Excel frequently displays “N/A” for the mode when you have continuous data. That happens because continuous data are unlikely to have exactly duplicated values, a requirement for the mode. Thanks to a data collection artifact, my data are continuous, but Excel displays the mode anyway. The study’s nurse collected the underlying data in inches and pounds, rounded them to the nearest unit, and converted them to their metric equivalents. That process produced clumps of rounded values. However, the mode really is not a good measure for these data.

Related post : Data Types and How to Graph Them

Central Tendency for our Descriptive Statistics Example

What can we learn by comparing the mean and median for both variables? For the height data, they are virtually equal, 1.51m and 1.50m, respectively. For symmetric distributions, the mean and median will be very close together. That’s a good sign that the heights follow a symmetric distribution, making the mean a good choice. The mean tells us that the height distribution centers on 1.51m.

However, there is a difference between the weight mean (46.3kg) and median (44.9kg). When the mean is greater than the median, it indicates that the distribution is right-skewed. We should use the median for these data. Half the data points fall above 44.9kg, and half fall below.

For more information about the different measures of central tendency, their calculations, how data types and distribution properties affect them, graphical representations, and when to use each type, read my post about Measures of Central Tendency .

Measures of Dispersion (Standard Deviation, Variance, Range)

Previously, you saw how a measure of central tendency indicates where most observations fall. Measures of dispersion indicate how closely clustered or loosely spread the data points fall around the center. Excel presents three measures of dispersion. In general, as their values increase, data points spread out further from the center (i.e., the distribution becomes broader).

Standard Deviation : The standard or typical difference between each data point and the mean. This measure uses the original units of the data, simplifying interpretation. Hence, analysts use this measure of variability the most frequently. The standard deviation is the square root of the variance.
Variance : The average squared difference of the values from the mean. Because the calculations use squared differences, the variance is in squared units rather than the original data units. While higher values of the variance indicate greater variability, there is no intuitive interpretation for specific values. Read more about the variance .
Range : The difference between the largest and smallest values in a dataset. The range is easy to understand but it is based on only the two most extreme values in the dataset, making it very susceptible to outliers. Additionally, the size of the dataset affects the range. As the sample size increases, the range tends to expand. Consequently, use the range to compare variability only when the sample sizes are similar. Read more about the range .

Typically, use the standard deviation . When you have fairly skewed data, consider using the interquartile range (IQR) , which Excel doesn’t provide, unfortunately.

Variability for our Descriptive Statistics Example

For the height data, the standard deviation is 0.07m (7cm). The typical height falls 7cm from the mean of 1.51m. The range tells us that the spread from the tallest to the shortest is 0.33m (33cm). You can draw similar conclusions from the weight data.

It might be tempting to compare the variability between heights and weights using the standard deviations. However, their standard deviations use different units, M and kg, making a direct comparison impossible. However, for some data, you can compare their coefficients of variation, which is easy to calculate using the standard deviation and means. For more information, read my post about the coefficient of variation .

For more information about the different measures of variability, their calculations, and when to use each type, read my post about Measures of Variability .

Distribution Shape Properties: Kurtosis and Skewness

Kurtosis and skewness are two measures that help you understand the general properties of your data’s distribution. These measures compare your distribution’s shape to a symmetric distribution and the normal distribution .

When either kurtosis or skewness significantly deviate from zero, it might indicate that your data do not follow a normal distribution. However, use a normality test or a normal distribution plot to make that determination.

I find that histograms present the same information more intuitively. However, graph axes and bin sizes can be manipulated to exaggerate or deemphasize characteristics while these statistics are completely objective.

Related posts : Using Histograms to Understand Your Data and Manually Adjusting Your Graph Axes

Kurtosis indicates how the peaks and tails of your distribution compare to the normal distribution. Is the peak taller or shorter than the normal distribution? Are the tails thicker or thinner? In the table, the red distributions have positive and negative kurtosis values while the blue distributions have a zero kurtosis value for comparison. For more details about this statistic, read my post about Kurtosis .


Zero	Consistent with a normal distribution
Positive	Thicker tails than the normal distribution
Negative	Thinner tails than the normal distribution

For our example data, height has a kurtosis of -0.35. This value is close to zero, indicating that the tails are consistent with the normal distribution. However, weight has a kurtosis of 1.15, suggesting the tails are thicker than the normal distribution.

Skewness indicates the symmetry of your data’s distribution. Skewed data are asymmetric. The terms right-skewed and left-skewed indicate the direction in which the long tail points on a distribution curve. Learn more about skewed distributions .


Zero	A perfectly symmetric distribution
Positive	Right-skewed data
Negative	Left-skewed data

Note that a U-shaped distribution can be symmetric even though it is inverted compared to the normal distribution.

For our example data, height has a skewness of 0.11. This value is close to zero, signifying that these data have a symmetric distribution. However, weight has a skewness of 1.05, which indicates it is right-skewed.

The relative locations of the mean and median and these distribution properties paint a consistent picture of these two variables. For the height data, the mean and median are nearly equal, and kurtosis and skewness are both virtually zero. These measures collectively imply that the heights follow a symmetric distribution consistent with the normal distribution.

Conversely, the weight data have a mean that is higher than the median, a positive skew value, and a positive kurtosis value. These values suggest that the weights follow an asymmetric, right-skewed distribution that is not consistent with the normal distribution.

Minimum and Maximum

The minimum and maximum values in your dataset can help you understand where your data fall. For our example data, the heights fall between 1.33 – 1.66 M, while the weights fall between 29.26 – 80.74 kg. Additionally, these values can help you identify outliers. Frequently, data entry errors create values that fall outside the range of valid data. Look at the minimum and maximum values and see if they make sense for your data!

Related post : Five Ways to Find Outliers in Your Data

Sum and Count

The sum is simply the sum of all values for each variable. I’ve never found this to be helpful, but perhaps it will be for you. The count is the number of observations for each variable. Use this value to determine whether the sample size is what you expected. Both the height and weight variables have 88 observations.

Precision of the Mean: Standard Error and the Confidence Interval

The standard error and the confidence interval assess how precisely your sample mean estimates the population mean. A relatively precise estimate indicates that your sample estimate is likely to be close to the actual population value. Conversely, an imprecise estimate tends to be further away from the correct population value.

Technically, neither of the values belong in the descriptive statistics output because they use your sample data to infer the properties of a larger population (inferential statistics). Descriptive statistics only describes your data without considering a population. However, Excel includes them in the output, so I’ll interpret them here.

Be aware that inferential statistics impose additional requirements on data collection methodologies that do not apply to descriptive statistics. For example, you must use a representative sampling methodology, such as random sampling; otherwise, these measures are invalid.

For more information, read my post about the differences between descriptive and inferential statistics .

Standard Error of the Mean

The standard error of the mean is the standard deviation of the sampling distribution of the mean. What?!

If you took many samples from the same population and calculated each sample’s mean, you’d produce a distribution of sample means. That distribution has a standard deviation, which is the standard error of the mean.

Smaller standard errors indicate that your sample provides a more precise estimate of the population value. Unfortunately, there is no intuitive interpretation of these values. However, the calculations for confidence intervals (CIs) incorporate the standard error, and CIs are much easier to interpret. So, focus on the CIs and don’t worry about the standard errors!

Related post : Standard Error of the Mean

Confidence Interval (CI) of the Mean

A confidence interval of the mean is a range of values that a population mean is likely to fall within. Because of random sampling error, you know that your sample mean is unlikely to equal the population mean, but how large is that difference? CIs help you answer that question by providing a range of probable values for the population mean.

Narrow CIs indicate more precise estimates of the population mean. In other words, you can expect your sample mean to be relatively close to the population mean.

Excel doesn’t provide the range, but it does display the number to add and subtract from your mean to calculate the confidence interval.

For the height data, Excel displays 0.015530282, which I’m rounding to 0.02. To calculate the CI, take the average height and +/- this value. In other words, 1.51 +/- 0.02 creates a CI of 1.49 – 1.53. We can be confident that the mean height for this population falls between these two values.

Using the same process, the confidence interval for weight is [43.98 48.68]. We can be confident that the mean weight for the population falls between these values.

If you want to know more about standard errors, confidence intervals, and confidence levels, read my post about How Confidence Intervals Work .

Histograms of our Descriptive Statistics Data

Let’s see the histograms for our example data. These graphs are not a part of Excel’s descriptive statistics. However, my suggestion is that you graph your data first and then study the numbers. All the statistics in this post describe the data that created the graphs below.

Are there any surprises?

For myself, I expected the height data to be more perfectly symmetrical. However, they are very slightly skewed to the right. The weight data are more right skewed, consistent with the descriptive statistics.

While the Descriptive Statistics analysis can’t assess correlation, read my post about Using Excel to Calculate Correlation to evaluate the relationship between these two variables!

Reader Interactions

September 7, 2023 at 2:38 am

I find very helpful to do my practical assignment.

January 26, 2022 at 11:38 am

I love your books and blogs. I am a layman when it comes to statistics. I like to use Excel to analyze interesting pursuits like the effectiveness of our community’s cane toad removal efforts and my efforts to improve air gun target shooting. Quick question: Can Excel produce the “predicted R-squared” you describe to determine overfitting? I don’t see it; just the “adjusted R-squared” to evaluate when too many independent variables are being used. If it doesn’t offer “predicted R-squared” directly, are there formulas I can use to calculate it?

January 26, 2022 at 5:34 pm

Hi Richard,

I love how statistics are usable in so many situations. It’s fantastic you’ve found ways in your personal life and community!

Unfortunately, I don’t believe that Excel can calculate predicted R-square as a built-in function. There are some 3rd party Excel add-ons that might be able to calculate it. I’m not sure. You could probably create an Excel formula yourself to find the answer. I might take a stab at that some point, but my to do list is a bit long!

I’m sorry I didn’t have a better answer for you!

January 23, 2022 at 10:53 pm

Thank you Jim! I don’t know if my question is appropriate to this post, so please disregard if thats the case. I used an online calculator to find a sample size, with a 95% confidence level and 5% confidence interval. Now I collected the data and have my sample mean, and would like to report it estimating what the population mean would probably be like. And I don’t know if I should calculate de confidence interval using the 5% that I used in the sample size calculation (mean +/- 5% of the mean), or if I should use the number reported by Excel to calculate the 95% confidence interval, which you discuss in your post (mean +/- number reported by Excel). Thank you again.

January 25, 2022 at 2:51 pm

I’m not sure what you mean when you say you looked up both a 95% confidence level and a 5% confidence interval. Do you mean a 95% confidence level and a 5% significance level? Those are two different forms of the same things and those values fit together. The significance level = 1 – confidence level. So, if you use a confidence level of 95%, that corresponds to a significance level of 5% in a hypothesis test.

So, I’m not entirely sure what you mean there.

However, in terms of reporting for the confidence interval specifically, you report the confidence level, which is almost always 95%. Here is how the APA says you should report CIs. From their manual,

“Use the format 95% CI [LL, UL] where LL is the lower limit of the confidence interval and UL is the upper limit. For example, one might report: 95% CI [5.62, 8.31].”

Even if you’re not required to use the APA format, you’ll be on solid ground by using it. Depending on the knowledge of your audience, you could follow that up with a fleshed-out interpretation, such as the following for the APA’s example:

The results indicate there is a 95% confidence level that the population mean falls between 5.62 and 8.31.

I hope that answers your questions!

January 23, 2022 at 4:04 pm

Hello Jim, Thank you for this post! I have a question about the Confidence Interval (95%) number that excel provides. Is it just applicable to estimate the population mean? I am wondering how to provide a confidence interval for proportions observed in the sample, for example the number of cases that are within a certain height range (to use your example) so that we can generalize the results to the population. I wanted to know which confidence interval to use if I wanted to report that we are 95% certain that (roughtly) 35% (+/- confidence interval number) of the population’s height will be between 1.45-1.51 (looking at the distribution of your histogram above). Will this be a different CI – perhaps what is used to calculate the sample size?

January 23, 2022 at 5:03 pm

Confidence intervals are only applicable to inferential statistics. Inferential statistics are when you use a sample to generalize to a population. In other words, you’re using sample characteristics to infer the properties of a population. As I mention in this post, it’s not accurate for Excel to include CIs in their descriptive statistics, which adds to the confusion! Inferential statistics need to account for the sampling error, which is the difference between your sample and the population. CIs are one way of doing that. So, if you want to generalize to a population, then you’re performing inferential statistics and CIs are appropriate.

Descriptive statistics is when you’re just describing the sample that you measured. There’s no uncertainty because you’ve measured everyone in the sample. Hence, there’s no reason at all to use a CI or hypothesis testing. You know the sample exactly. So, if you are not generalizing to a population and just want to understand the sample itself, don’t use CIs.

There are confidence intervals for population parameters other than the mean. You can obtain them for proportions, standard deviations, and so on. They just involve different calculations and data types. For proportions, you need binary data. For example, if you had pass/fail data, those are binary. You could collect a random sample and calculation the proportion of those who passed out of the total number. Additionally, you could obtain a CI for the proportion which gives you a range of likely values for the population proportion. In this example, you are again generalizing from the sample to the population. Hence, CIs are appropriate.

You could convert the continuous height data to binary data. For example, all heights greater than X could be considered “tall” while all heights lower than X are “not tall.” You’d have binary data with the two possible values of tall/not-tall. You could then calculate a proportion for those who are tall and get a CI for that proportion.

December 1, 2021 at 6:44 pm

Thank you, Jim! In my first semester of a doctoral program, and it has been 20+ years since I was in a stats class. I recommended that my professor link to your blog because it is very helpful for our intro course and a good companion to the textbook. I have this bookmarked for the future.

May 28, 2021 at 10:40 am

or in simple terms I want to ask what is the difference between SE= SD/sqrt N and SE(m)= Sqrt (2MSE/r)…and what they both interpret.

May 28, 2021 at 4:53 am

Thank you sir! I read your recommended post and SEM post also, again very nicely explained. I could now understand the line: “The standard deviation is the variability of individual data points around the sample mean. The standard error of the mean is the variability of sample means in the sampling distribution of means.”

But again my question is standard error of the mean is given in the end of the ANOVA and I can understand that it is a kind of variability measures for the different sample means in the sampling distribution and is used for further calculations.

but what about the standard error of the sample mean (individual sample only)…… many research articles have mentioned the SE for individual sample means and also for which we can also go for the standard deviation….. in spss descriptive statistics both SD and SE are given for individual sample means. after calculations I find this SE of sampling mean given there = SD for the sample mean/sqrt of number of replications or individual units in that sample……which is similar to SEM formula where formula is SD/sqrt Number of samples.

so my quarry is what does this SE for sample mean indicates.

May 28, 2021 at 11:21 pm

Hi Himanshu,

I *think* I see where some of your confusion is but I’m not sure.

Let me clarify. You’re seeing the standard error of the mean in an ANOVA context and you’re thinking it applies to the multiple means that you’re analyzing? If so, that’s not correct, although I can see how that would seem to make sense in that context! The F-test itself assesses the variability of the group means. To read how that works, read my post about the F-test in ANOVA . That does involve assessing both the variability of the group means and data points around their mean.

However, that is different from my discussion about the standard error of the mean. These standard errors are for individual sample means. Although you can have them for the group means in ANOVA too. But, in my post about the standard error of the mean, I’m talking about them from the standpoint of an individual sample. The distribution of means I’m referring to in that context is the sampling distribution (not the multiple means in ANOVA). You can have only one sample mean but the procedure still estimates a sampling distribution.

So, while reading my post about the standard error of the mean, keep in mind that I AM referring to individual sample means–exactly what you’re asking about! I hope that will clarify that aspect for you.

Yes, the standard error for an individual sample mean is the standard deviation/square root of the sample size. Again, that formula is in my other post.

I’m not familiar with SE(m)= Sqrt (2MSE/r). I don’t have SPSS so I’m not sure what that is in relation to. Sorry.

If I’m misunderstanding what you’re unsure about, please clarify!

May 21, 2021 at 4:57 pm

When we talk about skewness , we talk about right tail and left tail(we divide distribution in two parts). if right tail is long then we say right skewed else left skewed.

in case of unimodal data , we divide distribution in two parts by looking at peak. right side of peak will be considered as right tail and left side of peak will be considered as left tail. so here, mode is point which divide distribution in two parts.

but in case of bimodal data , if we divide two parts using either of mode then it will not look symmetric even though my distribution can be symmetrical if i use other point like median to divide my distribution in two parts.

so , i am getting confused that am i interpreting rightly that in case of unimodal we divide distribution by looking at peak (mode) and then compare two parts to get idea of skewness or is there any other technique which we use to divide distribution in two parts?

Thanks…

May 20, 2021 at 9:12 am

Respected Sir Greetings any reply to this comment please

Stay Safe Best wishes

May 20, 2021 at 2:52 pm

Somehow your previous question slipped through the cracks! I’ll be answering momentarily!

May 16, 2021 at 10:19 am

Hello sir Greetings of the day

Here I am with one more quarry regarding the descriptive statistics.

1. Sir What is the difference between the Standard deviation (SD) and Standard Error (SE). Suppose we have given 3 treatments to a population with 5 Replication each. As of now what I have understood is : a.) we calculate SD for each treatment mean and write mean of 5 replication in a given respective treatment +- SD of respective treatment in the table b.) SE or SEM is calculated in ANOVA when it is performed for all the treatment and is used for the calculation of LSD. But in many research papers they use to mention mean +- SE in many places with the treatment mean instead of SD. Also in SPSS, the descriptive statistics provide both SD and SE for the treatment. So my question is how SE is calculated for treatment instead of whole of the population (different treatment in ANOVA as point b).

2. In excel 2016 there are two formulas given STDEV-S and STDEV-P which I think is STDEV -S is for sample and is actually SD and STDEV-P is for population is actually SE, Sample means each treatment (only 5 replications) and population means all the treatments (all the 3 treatments along with their respective 5 replication) in combination (population comprises all the treatments which we have given to the population)

Am I correct or not for the point 2?

Thank you and Regards

May 20, 2021 at 4:19 pm

The standard deviation is the variability of individual data points around the sample mean. The standard error of the mean is the variability of sample means in the sampling distribution of means. Specifically, if the standard error of the means is the standard deviation of the sampling distribution. Conversely, the standard deviation applies to the distribution of sample values.

Statistical procedures use the standard error of the mean to calculate p-values and confidence intervals. Typically, you don’t interpret them directly. It assess how precise your sample mean estimates the population mean.

There are different equations for the standard deviation depending on whether you’re using a sample to estimate a population (use STDEV -S) or whether you just want to know the standard deviation for a particular dataset and not use it to infer the properties of a larger population (use STDEV -P). For more information on that issue and the nature of the difference between the two formulas, read my post about Measures of Variability , which discusses all that. Note that STDEV -P is NOT the standard error.

So, you have three different calculation methods, standard deviations for a sample or a population (click link above), and the standard error of the mean, which is the sample standard deviation divided by the square root of the sample size.

I hope that helps!

March 18, 2021 at 4:21 pm

Do you have instructions on how to make graphs in excell?

March 19, 2021 at 3:10 pm

I currently don’t have posts about how to make graphs in Excel. However, I am expanding my Excel content all the time and will eventually explain how to create and interpret graphs in Excel. Was there a particular graph you’re interested in?

March 18, 2021 at 1:45 pm

Hola Jim, te leemos desde muchas partes del mundo; gracias por compartir tus conocimientos.

Saludos desde Colombia!

February 28, 2021 at 3:58 pm

Thanks! Very helpful – like the book I bought from you!

February 28, 2021 at 5:56 pm

Thank you, Dr. Muller! I’m also so glad to hear that my book was helpful! 🙂

February 25, 2021 at 5:23 pm

greatly appreciated..thank you very much..this is really helpful.

February 23, 2021 at 10:30 am

Hi Jim, There some errors in stating kurtosis for skewness and vice vera.

February 23, 2021 at 1:45 pm

Thank you Bal Ram! I’ve fixed that typo!

February 23, 2021 at 7:22 am

Your Descriptive Statistics in Excel manual is very good and applicable to my veterinary and agronomy students. For your information I bought your books Regression Analysis and Hypothesis Testing by Amazon. Greetings from Brazil.

February 22, 2021 at 3:51 am

Thanks a bunch Jim. You have always done it well. Quite appreciate.

Someone mentioned that you did a book on Minitab. Which book is that? I will like to have it since I have a Minitab but most lessons are either on SPSS or XLSTAT

February 22, 2021 at 3:39 pm

I have three books and all three use Minitab. In these books, I don’t teach the use of Minitab but I use it to perform the analyses, create the output and graphs, etc. My goal is that everyone can learn from them even if they don’t use Minitab. However, if you use Minitab, I’m sure you’ll get a little bit more!

To see my books, go to my webstore . My books are listed there and you can even get free samples of them, so you can get an idea of what they cover and how I use Minitab. I include a note about my usage of Minitab at the end of the Introduction section in each book.

Happy reading! Jim

February 22, 2021 at 2:44 am

Thank you so much Jim for the simplicity in your explanations and support towards our research problems. Stay blessed

February 22, 2021 at 3:25 pm

Hi Sulaina! I’m so glad it was helpful! You stay blessed as well! 🙂

February 22, 2021 at 2:00 am

I was looking for clear cut explanation of descriptive stats in excel and you explained with utmost clarity. Thanks a ton!

February 22, 2021 at 3:23 pm

You bet, Dhawal! So glad it was helpful!

February 22, 2021 at 1:38 am

Thank you so much for your elaborate exposition. This is very enlightening. You make statistics really enjoyable & functional in research

February 22, 2021 at 12:54 am

Excellent !! Jim !!! Thank you so much

February 22, 2021 at 3:22 pm

You’re very welcome, Janardhan!

February 22, 2021 at 12:50 am

Appreciated Jim. I bought your books but found the books are using Minitab. Can you create a version of your book using Excel. I understand Excel doesn’t have all of the capabilities of Minitab, but can you cover the topics that Excel is capable of, without using VBA?

Yes! My plan is to write a book that focuses on using Excel to perform statistical analysis.

February 22, 2021 at 12:43 am

Always very helpful! Appreciated Jim! Very clearly explained

February 22, 2021 at 12:47 am

Thanks, Bob!! 🙂

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Using Excel for qualitative data analysis

Using Excel for qualitative data analysis File type DOCX File size 80.23 KB

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Using Excel for qualitative data analysis (archived link)

This article, written by Susan Eliot for The Listening Resource, provides step-by-step guidance on using Excel as a tool to support the analysis of qualitative data for research or evaluation purposes.

Assumptions
Worksheet Template
Coding and Categorizing
Making Comparisons
Step-by-Step Guide

Eliot, S. The Listening Resource, (2011). Using excel for qualitative data analysis. Retrieved from website: http://www.qualitative-researcher.com/qualitative-analysis/using-excel-for-qualitative-data-analysis/

Data Science
Data Analysis
Data Visualization
Machine Learning
Deep Learning
Computer Vision
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AI ML DS Interview Series
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Data Engineering
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Data Analysis in Excel

Data Analysis with Excel is a detailed lesson that gives readers a clear understanding of the newest and most sophisticated functions offered by Microsoft Excel. It describes in detail how to use MS-capabilities Excel to carry out various Data Analysis Excel tasks. The guide includes a good amount of screenshots that step-by-step demonstrate how to use various features. One of the most used programs for Data Analysis Excel is Microsoft Excel. You can simply import, browse, clean, analyze, and display your data using this all-in-one data management tool.

In this article, we will explore each and everything of Data Analysis in Excel and learn about Data analysis excel.

Table of Content

What is Data Analysis?

Excel for data analysis, methods of data analysis, excel functions for data analysis, frequently asked questions(faqs).

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves a variety of techniques and methods, including Data Analysis Excel, for extracting insights from raw data, with the ultimate goal of gaining a deeper understanding of patterns, trends, relationships, and characteristics within the dataset. Utilizing tools like Data Analysis Excel can significantly enhance the efficiency and accuracy of the analysis process.

Data analysis with Excel is a common and accessible way for individuals and businesses to analyze and visualize data. Microsoft Excel provides a range of tools and functions for performing basic to advanced data analysis tasks. The software enables users to seamlessly import and organize data from various sources, facilitating a structured foundation for Data Analysis Excel.

Data cleaning becomes an intuitive process with Excel’s capabilities, allowing users to identify and rectify issues like missing values and duplicates. PivotTables, a hallmark feature, empower users to swiftly summarize and explore large datasets, providing dynamic insights through customizable cross-tabulations, making Data Analysis Excel an essential skill for professionals.

Any set of information may be graphically represented in a chart. A chart is a graphic representation of data that employs symbols to represent the data, such as bars in a bar chart or lines in a line chart. Data Analysis Excel offers several different chart types available for you to choose from, or you can use the Excel Recommended Charts option to look at charts specifically made for your data and choose one of those.

Step 1: Select a table. After that go to the Insert tab on the top of the ribbon then in the charts group select any chart. Here we are going to select a 3-D column chart.

Step 2: As you can see, the excel table has been converted to a 3-D column chart.

Conditional Formatting

Patterns and trends in your data may be highlighted with the help of conditional formatting. To use it in Data Analysis Excel, write rules that determine the format of cells based on their values. In Excel for Windows, conditional formatting can be applied to a set of cells, an Excel table, and even a PivotTable report. To execute conditional formatting, adhere to the instructions listed below.

Step 1: Select any column from the table. Here we are going to select a Quarter column. After that go to the home tab on the top of the ribbon and then in the styles group select conditional formatting and then in the highlight cells rule select Greater than an option.

Step 2: Then a greater than dialog box appears. Here first write the quarter value and then select the color.

Step 3: As you can see in the excel table Quarter column change the color of the values that are greater than 6.

Data analysis Excel requires sorting the data. A list of names may be arranged alphabetically, a list of sales numbers can be arranged from highest to lowest, or rows can be sorted by colors or icons. Sorting data makes it easier to immediately view and comprehend your data, organize and locate the facts you need, and ultimately help you make better decisions. Both columns and rows can be used to sort. You’ll utilize column sorts for the majority of your sorting. By text, numbers, dates, and times, a custom list, format, including cell color, font color, or icon set, you may sort data in one or more columns.

Step 1: Select any column from the table. Here we are going to select a Months column. After that go to the data tab on the top of the ribbon and then in the sort and filters group select sort.

Step 2: Then a sort dialog box appears. Here first select the column, then select sort on, and then Order. After that click OK.

Step 3: Now as you can see the months column is now arranged alphabetically.

You may use filtering to pull information from a given Range or table that satisfies the specified criteria in data analysis excel. This is a fast method of just showing the data you require. Data in a Range, table, or PivotTable may be filtered. You may use Selected Values to filter data. You may adjust your filtering options in the Custom AutoFilter dialogue box that displays when you click a Filter option or the Custom Filter link that is located at the end of the list of Filter options.

Step 1: Select any column from the table. Here we are going to select a Sales column. After that go to the data tab on the top of the ribbon and then in the sort and filters group select filter.

Step 2: The values in the sales column are then shown in a drop-down box. Here we are going to select a number of filters and then greater than.

Step 3: Then a custom auto filler dialog box appears. Here we are going to apply sales greater than 70 and then click OK.

Step 4: Now as you can see only the rows greater than 70 are shown.

=LEN quickly returns the character count in a given cell. The =LEN formula may be used to calculate the number of characters needed in a cell to distinguish between two different kinds of product Stock Keeping Units, as seen in the example above. When trying to discern between different Unique Identifiers, which might occasionally be lengthy and out of order, LEN is very crucial.

=LEN(Select Cell)

Step 1: If we want to see the length of cell A2, for that we need to write the function of length.

Step 2: Now as you can see it shows the length of the cell A2.

=TRIM function will remove all spaces from a cell, with the exception of single spaces between words. The most frequent application of this function is to get rid of trailing spaces. When content is copied verbatim from another source or when users insert spaces at the end of the text, this is normal.

=TRIM(Select Cell)

Step 1: If we want to remove all spaces from cell A2 , for that we need to write the function of trim.

Step 2: Now as you can see after using the trim function, it removes all spaces.

The Excel Text function “UPPER Function” will change the text to all capital letters (UPPERCASE). As a result, the function changes all of the characters in a text string input to upper case.

=UPPER(Text)

Text (mandatory parameter): This is the text that we wish to change to uppercase. Text can relate to a cell or be a text string.

Step 1: If we want to convert the A2 cell to upper text, for that we need to write the upper function.

Step 2: Now as you can see after using the upper function, the text is changed to the upper text.

Under Excel Text functions, the PROPER Function is listed. Any subsequent letters of text that come after a character other than a letter will also be capitalized by PROPER.

=PROPER(Text)

Text (mandatory parameter): A formula that returns text, a cell reference, or text in quote marks must surround the text you wish to partly capitalize.

Step 1: If we want to convert the A2 cell to proper text, for that we need to write the proper function.

Step 2: Now as you can see after using the proper function, the text is changed to the proper form.

The PROPER function changes the initial letter of every word, letters that follow digits, and other punctuation to uppercase. It could be where we least expect it. The characters for numbers and punctuation remain unaffected.

Excel has a built-in function called COUNTIF that counts the given cells. The COUNTIF function can be used in both straightforward and sophisticated applications in data analysis excel. The fundamental application of counting particular numbers and words is covered in this.

=COUNTIF(range,criteria) Range: The size of the cell range to count. Criteria: The standards by which cells are selected for counting.

Step 1: Use the COUNTIF function on the range B2:B20 to get the number of regions we have of each type.

Step 2: The COUNTIF function will now be used to count the different sorts of Regions in the range F5:F9.

Step 3: Now as you can see the 4 East Region has been correctly enumerated using the COUNTIF function.

An Excel built-in function called AVERAGEIF determines the average of a range depending on a true or false condition.

=AVERAGEIF(range, criteria, [average_range]) Range: The size of the cell range to count. Criteria: The standards by which cells are selected for counting. Average Range: The range in which the function computes the average is known as the average range. But the average range is not required.

Step 1: Use the AVERAGEIF function on the range B2:B10 to get the average speed of vehicles.

Step 2: The AVERAGEIF function will now be used to find the average of Vehicles in the range H4:H7.

Step 3: Now as you can see the 62.333 Car average has been correctly enumerated using the AVERAGEIF function.

A built-in Excel function called SUMIF determines if a condition is true or false before adding the values in a range.

=SUMIF(range, criteria, [sum_range]) Range: The size of the cell range to count. Criteria: The standards by which cells are selected for counting. Sum Range: The range that the function uses to calculate the total is known as the sum range.

Step 1: Use the SUMIF function on the range B2:B10 to get the sum of the vehicle’s speed.

Step 2: The SUMIF function will now be used to find the sum of Vehicles’ speed in the range H4:H7.

Step 3: Now as you can see the 187 Car sum has been correctly enumerated using the SUMIF function.

VLOOKUP is a built-in Excel function that permits searching across several columns.

=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]) Lookup_value: Choose the cell that will be used to input the search criteria. Table_array: The whole table range, which includes each and every cell. Col_index_num: The information being searched for. The column’s number, starting from the left, is the input. Range_lookup: FALSE if text (0), TRUE if numbers (1).

Step 1: To locate the Festival names depending on their search ID, use the VLOOKUP function. The Festival names in this instance are determined by their search ID.

Step 2: F5 was chosen as the lookup value . The search query is typed in this cell. Table array, in this case, A2:C20, is designated as the table’s range. The col index number is set to 3, which is entered. The information being searched is in the third column from the left. Range lookup is entered as 0 (False).

Step 3: The #N/A value is what the function returns. This is the result of the Search ID F5 having no value entered.

Step 4: The Homegrown Festival , which has Search ID 6, has been located through the VLOOKUP tool.

PIVOT TABLE

In order to create the required report, a pivot table is a statistics tool that condenses and reorganizes specific columns and rows of data in a spreadsheet or database table. The utility simply “pivots” or rotates the data to examine it from various angles rather than altering the spreadsheet or database itself.

Step 1: Select any cell and then go to the home tab and then select Pivot table.

Step 2: Create Pivot table dialog box appears here select the new worksheet and then click OK.

Step 3: Now you can see it creates a pivot table.

Step 4: Just drag the Country field to the row area and the Days field to the value area.

Step 5: Now you can see the proper pivot table with Country and days fields.

In conclusion, mastering Data Analysis Excel techniques can significantly enhance your ability to interpret and manage data effectively. By utilizing features such as conditional formatting, PivotTables, and various built-in functions, you can uncover valuable insights and make data-driven decisions with confidence. Whether you are a beginner or an experienced user, continually exploring and applying new Data Analysis Excel methods will keep you at the forefront of data analysis and ensure you make the most of this powerful tool.

How do you do data analysis on Excel?

Excel facilitates data analysis through functions like PivotTables, charts, and statistical functions. Import, clean, and visualize data to draw meaningful insights.

Which tool is used for data analysis in Excel?

Excel itself is the tool for data analysis. It provides functions like PivotTables, charts, and various statistical functions for comprehensive analysis.

How do you organize data in Excel for analysis?

Organize data in Excel by using tables, sorting, filtering, and creating PivotTables. These features help structure data for effective analysis and interpretation.

What is the course content of Excel data analysis?

An Excel data analysis course covers topics such as importing data, cleaning, using functions, creating charts, PivotTables, and advanced features for in-depth analysis.

What is analysis data table Excel?

An Analysis Data Table in Excel is a tool for exploring various input values in a formula to observe how changes impact the results, aiding in sensitivity analysis.

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Introducing Text Insights in Excel

Text Insights in Excel is an advanced feature designed to streamline and enhance data interpretation. This innovative tool allows you swiftly summarize rows of textual data, extracting valuable insights and themes with ease.

How It Works

Text Insights leverages the power of Microsoft Copilot to analyze text within your workbook. Whether you're looking to summarize customer feedback, categorize bug descriptions, or identify major themes from a dataset, it simplifies the process into quick, actionable insights.

Feature Benefits

Efficiency : Quickly summarize large volumes of text without manual effort.

Clarity : Gain a better understanding of your data and identify key themes through concise summaries.

Customization : Tailor the results to your specific needs with flexible prompting options.

Getting Started

In your workbook, open the sheet containing the text you wish to analyze.

Select Copilot from the Excel menu.

Type your request in the prompt box.

Submit your prompt to begin analyzing the text.

Once the analysis is complete, Review the summary provided and submit any additional prompts to further distill the data to your specifications.

Discover the potential of Text Insights in Excel to convert raw text into meaningful insights and uncover key themes. With its intuitive interface and robust data capabilities, it's an essential tool for more effective decision-making.

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How to automate Microsoft Excel with macros

How i made my own notification system with python to track stocks, the weather, and more, how you can combine python with excel to supercharge your spreadsheets, key takeaways.

Combine Excel and Python for enhanced data processing capabilities, easily import libraries via Anaconda for seamless analysis.
Automate processes with Python scripts, like merging spreadsheets, cleaning data, and building complex data dumps efficiently.
Python offers advanced data visualization tools like Matplotlib and Plotly that provide complete control over graphing data.

I do a lot of data analysis. I have done a significant amount of data processing throughout my time at university, and my Bachelor's thesis was essentially a data processing project. For all of it, I ended up using Python for some of the most complicated parts, and I found that with a combination of both Excel and Python, I was able to really improve my data processing capabilities and make it easy to enjoy the relative simplicity of Excel while still managing to benefit from the advanced features of Python.

If you want to use the version of Python that's actually built into Excel nowadays, it's pretty easy . It uses a few Python libraries provided by Anaconda . As for the use cases, you can run your Python scripts, perform data analysis, create charts, glance over spreadsheet insights, visualize your data with plots, and more.

In addition to core libraries that come with it, you also have the flexibility to import more libraries through Anaconda. You need to use a Python import statement in a Python Excel cell to complete the import process. Microsoft recommends Astropy, Faker, IPython, NumPy, Pandas, Prince, qrcode, and a ton of others. You can head to the company’s official website to glance over the entire list. I use Pandas and NumPy the most, but there are countless tutorials online to help you get started.

To be honest, though, I just keep my sheets and my Python work separate. I'll work in Google Sheets or Excel and then, when I'm done, I'll export the file and start working in Python with it. I don't do them at the same time, but I can later reimport my data for further tweaking in Excel after I'm finished with my other processes.

How to record and use macros in Microsoft Excel

Dive into the world of macros to stream your workflow with Excel

3 Automation and efficiency

Forget complex formulas, just use scripts instead.

Excel is great and can be a quick and easy way to control your spreadsheets. With powerful formulas and a huge number of features, it's fantastic. However, if you take the time to learn Python, you can automate a lot of your processes without even need to look at anything complex. For things like data cleaning, merging spreadsheets, and building complex data dumps, Python is fantastic.

As an example, in the past, I have had to merge spreadsheets using a common key between two sheets and then take the data from spreadsheet B and append it to each relevant row in spreadsheet A. This is possible in Excel, but with Python, I have scripts that automate this process for me. I can simply run my script in the same folder as my spreadsheets and it does all the work for me, spitting out a completed CSV with everything that I need.

Of course, you can also read XLSX files with the Pandas library in Python, so Microsoft's own spreadsheet format is still fully supported.

2 Advanced data visualization

Matplotlib and plotly are both fantastic and significantly better than excel.

Frametime graph of Cyberpunk 2077 on the Steam Deck OLED

If you want to have complete control over how you visualize your data, then Python is a must. Python is used by data scientists all over the world, and for good reason. It's well-capable of producing a fantastic graph or visualizing data in a compelling way, and that's thanks to libraries like matplotlib. Nearly all of the data visualization I have ever done has been through matplotlib, and it gives you complete control over every aspect of your graph.

Like the rest of Python, this is absolutely a learning curve, but I strongly recommend you get to grips with it if you want to visualize your data in the best way possible. Matplotlib is a low-level library for visualization, but you can also try something like Plotly which is a more advanced, more modern library that might be easier to get into. Plotly has a high-level API that you can use for graphing, which makes things significantly easier if you're a newcomer to programming.

You can build your own tracking systems with Python, and it's surprisingly easy.

1 Seamless integration

Microsoft's python integration in excel is great.

Thanks to the integration of Python into Excel, it's never been easier to use the two together. You can simply select Formulas at the top and select Insert Python . You can also type =PY to invoke Python in a given cell. Python in Excel relies on the custom Python function x1 () to interface between Excel and Python, which works well with essential objects like ranges, tables, queries, and names.

On top of that, you can then use Excel for data entry, while then benefitting from the power of Python, and you can do it all from the one application. It's a fantastically powerful programming language that I highly recommend getting to grips with because it can make your data analysis so much easier.

Transform your Excel workflows with Python magic

Software and Services

Data Center

Housing market data, downloadable housing market data from redfin.

Redfin is a real estate brokerage, meaning we have direct access to data from local multiple listing services, as well as insight from our real estate agents across the country. That’s why we’re able to give you the earliest and most reliable data on the state of the housing market. We publish existing industry data faster, and offer additional data on tours and offers that no one else has. Using the tools below, you can visualize and download housing market data for metropolitan areas, cities, neighborhoods and zip codes across the nation.

Redfin Weekly Housing Market Data

This weekly data will be updated every Wednesday with new data for the prior week.

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Redfin Monthly Housing Market Data

To view additional housing market data at the local level, please visit our U.S. Housing Market Overview page here: https://www.redfin.com/us-housing-market

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Select the tab for the type of data that you’re looking for. Under each tab, you can filter results by metropolitan area, property type, month-over-month change, year-over-year change, and the time period. Each visualization will change with your selections.
After making your selections, click on the visualization and then hit the download button on the bottom right corner to get the data shown.
Alternatively, you can select the “Download” tab and download all of the data that we have available.
Check out this video for more information on how to use the Redfin Data Center. Contact [email protected] if you have questions.
Download region data here: National , Metro , State , County , City , Zip Code , Neighborhood

New data will be released monthly during the Wednesday of the third full week of the month. See the latest Redfin reports about monthly housing market performance.

Redfin Existing Home Sales

Redfin’s existing home sales data are stated as an annual rate, both seasonally adjusted and non-seasonally adjusted. These data are updated monthly on the Wednesday of the third full week of the month for the previous month.

The Redfin Home Price Index (RHPI)

The Redfin Home Price Index (RHPI) uses the repeat-sales pricing method to calculate how sale prices of single-family homes change over time. It measures sale prices of homes that sold during a given period, and how those prices have changed since the last time those same homes sold. The RHPI is seasonally adjusted and weights all home sales equally. It is published on the second-to-last Tuesday of every month. Click here for the full RHPI methodology.

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Where Data-Driven Decision-Making Can Go Wrong

Michael Luca
Amy C. Edmondson

When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question.

Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence.

By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.

Five pitfalls to avoid

Idea in Brief

The problem.

When managers are presented with internal data or an external study, all too often they either automatically accept its accuracy and relevance to their business or dismiss it out of hand.

Why It Happens

Leaders mistakenly conflate causation with correlation, underestimate the importance of sample size, focus on the wrong outcomes, misjudge generalizability, or overweight a specific result.

The Right Approach

Leaders should ask probing questions about the evidence in a rigorous discussion about its usefulness. They should create a psychologically safe environment so that participants will feel comfortable offering diverse points of view.

Let’s say you’re leading a meeting about the hourly pay of your company’s warehouse employees. For several years it has automatically been increased by small amounts to keep up with inflation. Citing a study of a large company that found that higher pay improved productivity so much that it boosted profits, someone on your team advocates for a different approach: a substantial raise of $2 an hour for all workers in the warehouse. What would you do?

Michael Luca is a professor of business administration and the director of the Technology and Society Initiative at Johns Hopkins University, Carey Business School.
Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School. Her latest book is Right Kind of Wrong: The Science of Failing Well (Atria Books, 2023).

Partner Center

Return to Zillow.com

What is a Zestimate?

The Zestimate® home valuation model is Zillow’s estimate of a home’s market value. A Zestimate incorporates public, MLS and user-submitted data into Zillow’s proprietary formula, also taking into account home facts, location and market trends. It is not an appraisal and can’t be used in place of an appraisal.

How accurate is the Zestimate?

The nationwide median error rate for the Zestimate for on-market homes is 2.4%, while the Zestimate for off-market homes has a median error rate of 7.49%. The Zestimate’s accuracy depends on the availability of data in a home’s area. Some areas have more detailed home information available — such as square footage and number of bedrooms or bathrooms — and others do not. The more data available, the more accurate the Zestimate value will be.

These tables break down the accuracy of Zestimates for both active listings and off-market listings.

Active listings accuracy

Last updated: April 27, 2023

Note: The Zestimate’s accuracy is computed by comparing the final sale price to the Zestimate that was published on or just prior to the sale date.

Download an Excel spreadsheet of this data .

How is the Zestimate calculated?

Zillow publishes Zestimate home valuations for 104 million homes across the country, and uses state of the art statistical and machine learning models that can examine hundreds of data points for each individual home.

To calculate a Zestimate, Zillow uses a sophisticated neural network-based model that incorporates data from county and tax assessor records and direct feeds from hundreds of multiple listing services and brokerages. The Zestimate also incorporates:

Home characteristics including square footage, location or the number of bathrooms.
On-market data such as listing price, description, comparable homes in the area and days on the market
Off-market data — tax assessments, prior sales and other publicly available records
Market trends, including seasonal changes in demand

Currently, we have data for over 110 million U.S. homes and we publish Zestimates for 104 million of them.

What changes are in the latest Zestimate?

The latest Zestimate model is our most accurate Zestimate yet. It’s based on a neural network model and uses even more historical data to produce off-market home valuations. This means the Zestimate is more responsive to market trends & seasonality that may affect a home’s market value. We also reduced overall errors and processing time in the Zestimate.

My Zestimate seems too low or too high. What gives?

The amount of data we have for your home and homes in your area directly affects the Zestimate’s accuracy, including the amount of demand in your area for homes. If the data is incorrect or incomplete, update your home facts — this may affect your Zestimate. To ensure the most accurate Zestimate, consider reporting any home updates to your local tax assessor. Unreported additions, updates and remodels aren’t reflected in the Zestimate.

Check that your tax history and price history (the sale price and date you bought your home) are accurate on Zillow. If data is missing or incorrect, let us know .

Be aware that the model that creates the Zestimate factors in changing market trends, including seasonal fluctuations in demand. So in some cases that may be the reason for a change in your Zestimate.

I just listed my home for sale. Why did my Zestimate change?

When a home goes on the market, new data can be incorporated into the Zestimate algorithm. In the simplest terms, the Zestimate for on-market homes includes listing data that provides valuable signals about the home’s eventual sale price. This data isn’t available for off-market homes.

My home is on the market. Why is the Zestimate so far off?

Properties that have been listed for a full year transition to off-market valuations because they have been listed longer than normal for that local market. This can result in a large difference between the list price and the Zestimate.

I just changed my home facts. When will my Zestimate update?

Updates to your home facts are factored into the Zestimate. However, if the updates are not significant enough to affect the home’s value (eg: paint colors), your Zestimate may not change. Zestimates for all homes update multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

How are changes to my home facts (like an additional bedroom or bathroom) valued?

The Zestimate is based on complex and proprietary algorithms that can incorporate millions of data points. The algorithms determine the approximate added value that an additional bedroom or bathroom contributes, though the amount of the change depends on many factors, including local market trends, location and other home facts.

Is the Zestimate an appraisal?

No. The Zestimate is not an appraisal and can’t be used in place of an appraisal. It is a computer-generated estimate of the value of a home today, given the available data.

We encourage buyers, sellers and homeowners to supplement the Zestimate with other research, such as visiting the home, getting a professional appraisal of the home, or requesting a comparative market analysis (CMA) from a real estate agent.

Why do I see home values for the past?

We generate historical Zestimates for most homes if we have sufficient data to do so.

Do you ever change historical Zestimates?

We occasionally recalculate historical Zestimate values along with major data upgrades or improvements to the algorithm. These recalculations are based on a variety of considerations and, therefore, not every new algorithm release will get a corresponding update of historical values.

However, we never allow future information to influence a historical Zestimate (for example, a sale in 2019 could not influence a 2018 Zestimate). Historical Zestimates only use information known prior to the date of that Zestimate.

Does the Zestimate algorithm ever change?

Yes — Zillow’s team of researchers and engineers work every day to make the Zestimate more accurate. Since Zillow’s founding in 2006, we have deployed multiple major Zestimate algorithm updates and other incremental improvements are consistently released between major upgrades.

How often are Zestimates for homes updated?

We refresh Zestimates for all homes multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

Are foreclosure sales included in the Zestimate algorithm?

No. The Zestimate is intended to provide an estimate of the price that a home would fetch if sold for its full value, where the sale isn’t for partial ownership of the property or between family members. Our extensive analysis of foreclosure resale transactions supports the conclusion that these sales are generally made at substantial discounts compared to non-foreclosure sales. For this reason, the Zestimate does not incorporate data about these sales.

Who calculates the Zestimate? Can someone tamper with my home’s Zestimate?

The Zestimate is an automated valuation model calculated by a software process. It’s not possible to manually alter the Zestimate for a specific property.

Can the Zestimate be updated?

Yes. The Zestimate’s accuracy depends on the amount of data we have for the home. Public records can be outdated or lag behind what homeowners and real estate agents know about a property, so it’s best to update your home facts and fix any incorrect or incomplete information — this will help make your Zestimate as accurate as possible.

You can also add info about the architectural style, roof type, heat source, building amenities and more. Remember: updating home information doesn’t guarantee an increase in the value of Zestimate, but will increase the Zestimate’s accuracy.

Does Zillow delete Zestimates? Can I have my Zestimate reviewed if I believe there are errors?

We do not delete Zestimates. However, for some homes we may not have enough data to provide a home valuation that meets our standards for accuracy. In these instances, we do not publish the Zestimate until more data can be obtained. The Zestimate is designed to be a neutral estimate of the fair market value of a home, based on publicly available and user-submitted data. For this purpose, it is important that the Zestimate is based on information about all homes (e.g., beds, baths, square footage, lot size, tax assessment, prior sale price) and that the algorithm itself is consistently applied to all homes in a similar manner.

I don’t know of any homes that have sold recently in my area. How are you calculating my Zestimate?

Zestimates rely on much more than comparable sales in a given area. The home’s physical attributes, historical information and on-market data all factor into the final calculation. The more we know about homes in an area (including your home), the better the Zestimate. Our models can find neighborhoods similar to yours and use sales in those areas to extrapolate trends in your housing market. Our estimating method differs from that of a comparative market analysis completed by a real estate agent. We use data from a geographical area that is much larger than your neighborhood — up to the size of a county — to help calculate the Zestimate. Though there may not be any recent sales in your neighborhood, even a few sales in the area allow us to extrapolate trends in the local housing market.

I’m trying to sell my home and I think my Zestimate should be higher.

The Zestimate was created to give customers more information about homes and the housing market. It is intended to provide user-friendly data to promote transparent real estate markets and allow people to make more informed decisions — it should not be used to drive up the price of a home. Zestimates are designed to track the market, not drive it.

Can I use the Zestimate to get a loan?

No. The Zestimate is an automated value model and not an appraisal. Most lending professionals and institutions will only use professional appraisals when making loan-related decisions.

I have two Zestimates for my home. How do I fix this?

If you see two Zestimates for the same property, please let us know by visiting the Zillow Help Center and s e lecting Submit a request. You may see more than one Zestimate for your address if you are a homeowner with multiple parcels of land. Zillow matches the parcels on record with the county. If you officially combine parcels, the county will send us updated information.

What’s the Estimated Sale Range?

While the Zestimate is the estimated market value for an individual home, the Estimated Sale Range describes the range in which a sale price is predicted to fall, including low and high estimated values. For example, a Zestimate may be $260,503, while the Estimated Sale Range is $226,638 to $307,394. This range can vary for different homes and regions. A wider range generally indicates a more uncertain Zestimate, which might be the result of unique home factors or less data available for the region or that particular home. It’s important to consider the size of the Estimated Sale Range because it offers important context about the Zestimate’s anticipated accuracy.

How can real estate professionals work with the Zestimate?

Millions of consumers visit Zillow every month. When combined with the guidance of real estate professionals, the Zestimate can help consumers make more informed financial decisions about their homes. Real estate professionals can also help their clients claim their home on Zillow, update the home facts and account for any work they have done on the property. A home’s Zillow listing is often the first impression for prospective buyers, and accurate information helps attract interest.

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Harris Energizes Democrats in Transformed Presidential Race

1. the presidential matchup: harris, trump, kennedy, table of contents.

Other findings: Both Harris and Trump are viewed more favorably than a few months ago
Voting preferences among demographic groups
How have voters shifted their preferences since July?
Harris’ supporters back her more strongly than Biden’s did last month
Large gap in motivation to vote emerges between the candidates’ younger supporters
Harris and Trump have gained ground with their own coalitions
Share of ‘double negatives’ drops significantly with change in presidential candidates
Views of Biden have changed little since his withdrawal from the 2024 presidential race
Acknowledgments
The American Trends Panel survey methodology

Nationally, Vice President Kamala Harris and former President Donald Trump are essentially tied among registered voters in the current snapshot of the presidential race: 46% prefer Harris, 45% prefer Trump and 7% prefer Robert F. Kennedy Jr.

Following Biden’s exit from the race, Trump’s support among voters has remained largely steady (44% backed him in July against Biden, while 45% back him against Harris today). However, Harris’ support is 6 percentage points higher than Biden’s was in July . In addition to holding on to the support of those who backed Biden in July, Harris’ bump has largely come from those who had previously said they supported or leaned toward Kennedy.

Harris performs best among the same demographic groups as Biden. But this coalition of voters is now much more likely to say they strongly support her: In July, 43% of Biden’s supporters characterized their support as strong – today, 62% of Harris’ do.

Chart shows Black, Hispanic, Asian and younger voters back Harris by large margins, while Trump leads among older voters and those without a bachelor’s degree

Overall, many of the same voting patterns that were evident in the Biden-Trump matchup from July continue to be seen today. Harris fares better than Trump among younger voters, Black voters, Asian voters and voters with college degrees. By comparison, the former president does better among older voters, White voters and voters without a college degree.

But Harris performs better than Biden across many of these groups – making the race tighter than it was just a few weeks ago.

In July, women’s presidential preferences were split: 40% backed Biden, 40% preferred Trump and 17% favored Kennedy. With Harris at the top of the ticket, 49% of women voters now support her, while 42% favor Trump and 7% back Kennedy.
Among men, Trump draws a similar level of support as he did in the race against Biden (49% today, compared with 48% in July). But the share of men who now say they support Harris has grown (to 44% today, up from 38% last month). As a result, Trump’s 10-point lead among men has narrowed to a 5-point lead today.

Race and ethnicity

Harris has gained substantial ground over Biden’s position in July among Black, Hispanic and Asian voters. Most of this movement is attributable to declining shares of support for Kennedy. Trump performs similarly among these groups as he did in July.

77% of Black voters support or lean toward Harris. This compares with 64% of Black voters who said they backed Biden a few weeks ago. Trump’s support is unchanged (13% then vs. 13% today). And while 21% of Black voters supported Kennedy in July, this has dropped to 7% in the latest survey.
Hispanic voters now favor Harris over Trump by a 17-point margin (52% to 35%). In July, Biden and Trump were tied among Hispanic voters with 36% each.
By about two-to-one, Asian voters support Harris (62%) over Trump (28%). Trump’s support among this group is essentially unchanged since July, but the share of Asian voters backing Harris is 15 points higher than the share who backed Biden in July.
On balance, White voters continue to back Trump (52% Trump, 41% Harris), though that margin is somewhat narrower than it was in the July matchup against Biden (50% Trump, 36% Biden).

While the age patterns present in the Harris-Trump matchup remain broadly the same as those in the Biden-Trump matchup in July, Harris performs better across age groups than Biden did last month. That improvement is somewhat more pronounced among voters under 50 than among older voters.

Today, 57% of voters under 30 say they support Harris, while 29% support Trump and 12% prefer Kennedy. In July, 48% of these voters said they backed Biden. Trump’s support among this group is essentially unchanged. And 12% now back Kennedy, down from 22% in July.
Voters ages 30 to 49 are now about evenly split (45% Harris, 43% Trump). This is a shift from a narrow Trump lead among this group in July.
Voters ages 50 and older continue to tilt toward Trump (50% Trump vs. 44% Harris).

With Harris now at the top of the Democratic ticket, the race has become tighter.

Chart shows Since Biden’s exit, many who previously supported RFK Jr. have shifted preferences, with most of these voters now backing Harris

Much of this is the result of shifting preferences among registered voters who, in July, said they favored Kennedy over Trump or Biden.

Among the same group of voters surveyed in July and early August, 97% of those who backed Biden a few weeks ago say they support or lean toward Harris today. Similarly, Trump holds on to 95% of those who supported him a few weeks ago.

But there has been far more movement among voters who previously expressed support for Kennedy. While Kennedy holds on to 39% of those who backed him in July, the majority of these supporters now prefer one of the two major party candidates: By about two-to-one, those voters are more likely to have moved to Harris (39%) than Trump (20%). This pattern is evident across most voting subgroups.

In July, Trump’s voters were far more likely than Biden’s voters to characterize their support for their candidate as “strong” (63% vs. 43%). But that gap is no longer present in the Harris-Trump matchup.

Chart shows ‘Strong’ support for Harris is now on par with Trump’s and is much higher than Biden’s was in July

Today, 62% of Harris voters say they strongly support her, while about a third (32%) say they moderately support her. Trump’s voters are just about as likely to say they strongly back him today as they were in July (64% today, 63% then).

Kennedy’s voters make up a smaller share of voters today than a month ago – and just 18% of his voters say they strongly support him, similar to the 15% who said the same in July.

Across demographic groups, strong support for Harris is higher than it was for Biden

Among women voters who supported Biden in July, 45% said they did so strongly. That has grown to 65% today among women voters who support Harris.

Chart shows Across demographic groups, Harris’ strong support far surpasses Biden’s a month ago

Increased intensity of support is similar among men voters who back the Democratic candidate: In July, 42% of men voters who supported Biden said they did so strongly. This has since grown to 59% of Harris’ voters who are men.

Across racial and ethnic groups, Harris’ supporters are more likely than Biden’s were to say they back their candidates strongly.

Among White voters, 43% who supported Biden in July did so strongly. Today, Harris’ strong support among White voters sits at 64%.

A near identical share of Harris’ Black supporters (65%) characterize their support for her as strong today. This is up from the 52% of Biden’s Black supporters who strongly backed him in July. Among Harris’ Hispanic supporters, 56% support her strongly, while 45% of Asian Harris voters feel the same. Strong support for Harris among these voters is also higher than it was for Biden in July.

Across all age groups, Harris’ strength of support is higher than Biden’s was. But the shift from Biden is less pronounced among older Democratic supporters than among younger groups.

Still, older Harris voters are more likely than younger Harris voters to describe their support as strong. For instance, 51% of Harris’ voters under 50 say they strongly support her, while 71% of Harris supporters ages 50 and older characterize their support as strong.

Today, about seven-in-ten of both Trump supporters (72%) and Harris supporters (70%) say they are extremely motivated to vote.

Motivation to vote is higher in both the Democratic and Republican coalitions than it was in July .

Chart shows Older voters remain more motivated to vote, but Harris’ younger supporters are more motivated than Trump’s

These shifts have occurred across groups but are more pronounced among younger voters.

Today, half of voters under 30 say they are extremely motivated to vote, up 16 points since July. Motivation is up 11 points among voters ages 30 to 49 and 50 to 64, and up 6 points among those ages 65 and older.

Among the youngest voters, the increased motivation to vote is nearly all driven by shifts among Democratic supporters.

In July, 38% of 18- to 29-year-old Trump voters said they were extremely motivated to vote. Today, a similar share of his voters (42%) report that level of motivation.
But 18- to 29-year-old Harris supporters are far more likely to say they are extremely motivated to vote than Biden’s supporters in this age group were about a month ago. Today, 61% of Harris’ voters under 30 say this. In July, 42% of voters under 30 who supported Biden said they were extremely motivated to vote.

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Data Analysis in Excel (A Comprehensive Guideline)
Go to the Data tab and select Data Analysis (This will be available once you activate the Analysis ToolPak add-in). A Data Analysis box will pop up. Select the Descriptive Statistics option and click OK. Set the input range and the output range and click OK. Check Summary statistics.
Analyze Data in Excel
Analyze Data in Excel empowers you to understand your data through high-level visual summaries, trends, and patterns. Simply click a cell in a data range, and then click the Analyze Data button on the Home tab. Analyze Data in Excel will analyze your data, and return interesting visuals about it in a task pane.
Best 10 Features for Data Analysis in Excel
Here's our very own senior data scientist, Tom, explaining these functions in a video: 1. Pivot tables and pivot charts. One of my favorite features in Excel is pivot charts and, as a close follow-up feature, pivot tables. Pivot charts visualize the data expressed in a pivot table, giving us insight at a glance.
The Ultimate Guide to Data Analysis in Excel
Click the Add-Ins category on the left. (If you are using Excel 2007, click the Microsoft Office Button, then click Excel Options.) From the Manage dropdown list, select Excel Add-ins, then click Go. In the Add-Ins dialog box, tick the Analysis ToolPak check box, then click OK. Notes:
How to Do Data Analysis in Excel: A Comprehensive Step-by-Step Guide
Step 1: Import Your Data. First, import your data into Excel. To import data, go to the "Data" tab and choose the source, such as a CSV file or an external database. Navigate to your file, and Excel will load the data into a new worksheet.
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Use the Analysis ToolPak to perform complex data analysis
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Data Analysis in Excel Course
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1. On the Data tab, in the Analysis group, click Data Analysis. Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in. 2. Select Regression and click OK. 3. Select the Y Range (A1:A8). This is the predictor variable (also called dependent variable).
Descriptive Statistics in Excel
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Data Analysis in Excel (A Comprehensive Guideline)

How to Analyze Data in Excel

Method 2 – Data Analysis Using Excel Charts

Method 3 – Apply Conditional Formatting to Analyze Data

Method 4 – A Pivot Table

Method 5 – Sorting Data in Excel

Method 6 – Filtering Data in Excel

Method 7 – Excel What-If Analysis Feature

Method 8 – Data Validation

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Method 11 – Using the Analysis ToolPak Add-in

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1. Pivot tables and pivot charts

2. Conditional formatting

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How to Do Data Analysis in Excel: A Comprehensive Step-by-Step Guide

Step-by-Step Tutorial on How to Do Data Analysis in Excel

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