How to Do Regression Analysis in Excel

Ever wonder if your ad spend is really driving sales, or how website traffic influences your free trial sign-ups? Regression analysis is a powerful statistical method that helps you answer these exact kinds of questions by measuring the relationship between different variables. This guide will walk you through, step by step, how to perform a regression analysis right inside Excel, transforming your spreadsheets into a powerful tool for finding actionable insights.

What Exactly Is Regression Analysis?

At its core, regression analysis helps you understand how a dependent variable (the main thing you're trying to predict or explain, like sales) changes when you change an independent variable (the factor you think has an influence, like ad spend). Think of it like drawing the "best fit" line through a series of data points on a graph. This line doesn't just show a trend, it creates a mathematical model you can use for forecasting.

For example, you could use it to:

• Predict future sales based on your projected marketing budget.

• Understand the drivers of customer satisfaction by analyzing survey data against operational metrics.

• Estimate the impact of pricing changes on product demand.

The goal is to move from just looking at data to understanding the relationships hiding within it. Excel makes this process surprisingly accessible, even if you don't consider yourself a statistician.

Preparing for Analysis: Enabling the Analysis ToolPak

Before you can do any analysis, you need to make sure Excel's built-in statistical toolkit is activated. This feature, called the Analysis ToolPak, is included with Excel but isn't enabled by default. It's a quick, one-time setup.

Here’s how to turn it on:

For Windows users:

1. Click on File in the top-left corner, then select Options at the bottom of the left-hand menu.

2. In the Excel Options window, click on Add-ins from the sidebar.

3. At the bottom of the window, you'll see a drop-down menu next to "Manage." Make sure it says Excel Add-ins and then click Go....

4. In the Add-ins pop-up, check the box next to Analysis ToolPak and click OK.

Once you’ve done this, a new "Data Analysis" button will appear under the Data tab on your Excel ribbon.

For Mac users:

1. Go to the Tools menu at the top of your screen.

2. Click on Excel Add-ins...

3. Check the box next to Analysis ToolPak and click OK.

You're now ready to start analyzing your data!

Step 1: Get Your Data Ready

The first rule of any data analysis is to organize your data correctly. For regression analysis in Excel, you'll want your data in columns. You need at least two columns:

• The Independent Variable (X-axis): This is the factor you believe is causing the change. It's the variable you are adjusting or observing. Sometimes it's called the predictor or explanatory variable.

• The Dependent Variable (Y-axis): This is the factor you want to predict or explain. Its value "depends" on the independent variable. It's often called the outcome or response variable.

Let's use a straightforward example. Imagine you're a marketing manager and you want to know the relationship between your monthly Facebook Ads spend and the number of leads generated. Your dataset might look something like this:

For your analysis to work, make sure your two columns are right next to each other. Your independent variable (Ad Spend) should be on the left, and your dependent variable (Leads) should be on the right.

Step 2: Run the Regression Analysis

With your data neatly organized and the ToolPak enabled, it's time to run the analysis.

1. Go to the Data tab and click the Data Analysis button on the right side of the ribbon.

2. A new window will pop up. Scroll down and select Regression, then click OK.

3. This opens the Regression dialog box. This might look intimidating, but you only need to fill in a few key fields:

Input Y Range:

This is your dependent variable (the outcome). Click the icon next to the field and select the entire column of data for "Leads Generated," including the header.

Input X Range:

This is your independent variable (the predictor). Click the icon and select the column of data for "Monthly Ad Spend," including the header here as well.

Labels:

Check this box. This tells Excel that the first row of your selection contains the column headers ("Ad Spend," "Leads"), not numerical data. It's a small step that makes your output report much easier to read.

Confidence Level:

You can leave this checked at 95%, which is the standard confidence level for most statistical analyses.

Output Options:

Choose where you want Excel to place the results. New Worksheet Ply is a good option as it keeps your analysis report separate and tidy. Then click OK.

Excel will instantly generate a new sheet with your regression summary output. Now comes the most important part: understanding what it all means.

Step 3: How to Interpret the Output Report

Excel's regression output is split into three main tables. Let's break down the most important numbers in each part and translate them into plain English.

Part 1: The Regression Statistics Table

This table gives you a high-level overview of how well your model fits the data.

• Multiple R: This is the Correlation Coefficient. It tells you the strength of the linear relationship between your two variables. The value ranges from -1 to 1. A value close to 1 means a strong positive relationship (as Ad Spend goes up, Leads go up), a value close to -1 means a strong negative relationship, and a value near 0 means there's little to no relationship.

• R Square: This is one of the most important numbers. It tells you the percentage of variation in your dependent variable (Leads) that is explained by your independent variable (Ad Spend). In our example, an R Square of 0.96 would mean that 96% of the variation in the number of leads we generate can be explained by our monthly ad spend. That's a very strong model!

• Adjusted R Square: This is a slightly modified version of R Square that accounts for the number of variables in your model. When you're only looking at one independent variable (like we are here), it will be very close to the R Square value. It becomes more useful when you start doing multiple regression with many variables.

Part 2: The ANOVA Table

ANOVA stands for Analysis of Variance. This table tells you if your overall model is statistically significant.

• Significance F: This is the key number in this section. It's the p-value for the overall model. A general rule of thumb is that if this number is less than 0.05, your model is statistically significant. A very small number (like 1.05E-05, which is 0.0000105) is excellent - it means it's extremely unlikely that the relationship you've observed in your data happened by random chance.

Part 3: The Coefficients Table

This is where you'll find the practical, actionable numbers for your prediction model.

• Intercept (under Coefficients): This is where your regression line crosses the Y-axis. In simpler terms, this is the predicted value of your dependent variable (Leads) if your independent variable (Ad Spend) was zero. So, if the Intercept is 15.3, the model predicts we would still generate about 15 leads even with $0 in ad spend.

• Variable Coefficient (e.g., "Monthly Ad Spend"): This is the heart of your analysis. It tells you how much your dependent variable is expected to change for every one-unit increase in your independent variable. If the coefficient for Ad Spend is 0.046, it means that for every additional $1 spent on Facebook Ads, we can expect to generate an additional 0.046 leads. Now you have a concrete number for ROI!

• P-value: Just like the Significance F for the overall model, each variable has its own p-value. Again, a p-value less than 0.05 suggests the variable is a meaningful addition to the model.

Putting it all together, the equation for our model becomes:

Leads Generated = 15.3 + (0.046 * Monthly Ad Spend)

Now you have a simple formula you can use to forecast how many leads you might get with a different ad budget!

Step 4: Visualize Your Regression Results

A numbers-heavy report is great, but a simple chart often tells the story more effectively. A scatter plot with a trendline is the perfect way to visualize your regression model.

1. Go back to your original data sheet.

2. Highlight both columns of your data, including the headers.

3. Go to the Insert tab and find the Charts group.

4. Click on the icon that looks like a scatter plot (it has a bunch of dots on it). Select the first option, the basic scatter chart.

5. Your chart will appear. Now, to add the regression line, right-click on any of the blue data points on the chart and select Add Trendline...

6. A "Format Trendline" pane will open on the right side. Ensure that Linear is selected.

7. To add the formula directly to your chart, scroll down in the pane and check the boxes for Display Equation on chart and Display R-squared value on chart.

You now have a clean, easy-to-understand visualization that shows the clear relationship between your ad spend and leads, backed by the very equation and R-squared value you just calculated.

Final Thoughts

Running a regression analysis in Excel may seem complex at first, but once you walk through the steps, you'll see it’s a systematic process. By understanding how to enable the Analysis ToolPak, prepare your data, and interpret the output, you can unlock a powerful way to understand the drivers of your business performance and make data-backed forecasts.

While Excel is a great tool for this, analysis can get even easier. It often takes hours to pull data from different platforms, clean it up in a spreadsheet, and painstakingly build these reports just to get an answer. Here at Graphed, we built a tool to automate that entire process. By connecting directly to your marketing and sales platforms like Google Analytics, Shopify, or Facebook Ads, we let you create real-time dashboards and get instant insights by just asking questions in plain English - no wrestling with output tables, interpreting coefficients, or taking courses needed.