What is Regression Analysis in Excel?

Have you ever wondered exactly how much your Facebook ad budget is impacting your monthly sales? Or how an increase in website traffic might affect the number of new sign-ups? Regression analysis is the statistical method that answers these kinds of questions, and you don’t need an advanced degree or fancy software to use it. You can do it right inside Microsoft Excel.

This tutorial will walk you through exactly what regression analysis is, why it's a valuable tool for any business owner or marketer, and how you can run one yourself in Excel, step by step. We'll even cover how to make sense of the results without getting lost in statistical jargon.

What is Regression Analysis, Anyway?

At its core, regression analysis helps you understand and measure the relationship between two or more variables. It looks for a cause-and-effect connection, where a change in one variable is associated with a change in another.

Think of it like trying to predict a house's price based on its square footage. Generally, the larger the house, the higher the price. Regression analysis finds the specific mathematical formula for that relationship. Once you have that formula, you can confidently predict the price of a 2,500-square-foot house, even if you’ve never seen one before.

To keep things clear, let's define two key terms:

Dependent Variable (Y): This is the main thing you are trying to predict or explain. It’s the "effect." In our examples, Sales Revenue, New Sign-ups, or House Price would be the dependent variables.
Independent Variable(s) (X): These are the factors you think influence your dependent variable. They are the "causes." For our examples, this would be Ad Spend, Website Traffic, or Square Footage.

There are two primary types of linear regression you'll encounter:

Simple Linear Regression: This involves one dependent variable and only one independent variable. (e.g., predicting Sales from Ad Spend alone).
Multiple Linear Regression: This involves one dependent variable and two or more independent variables. (e.g., predicting Sales from Ad Spend, Email Marketing Budget, and SEO efforts).

Excel allows you to perform both types with the same tool, giving you a powerful way to make forecasts and smarter business decisions.

Why Should You Use Regression Analysis?

Beyond satisfying statistical curiosity, regression analysis has incredibly practical applications for growing a business. It turns data from a list of historical numbers into a forward-looking decision-making tool.

Forecasting Sales and Revenue

This is one of the most common uses. By analyzing historical data, a business can build a model to forecast future performance. For instance, if you know that for every $100 you spend on ads, you generate an average of $450 in revenue, you can confidently set an ad budget of $5,000 for next month with a reasonable expectation of generating $22,500 in revenue.

Understanding What Drives Performance

Sometimes it’s not clear which of your efforts is making the biggest difference. Is your content marketing or your paid social campaign driving more conversions? A multiple regression analysis could show you which variable has the strongest impact. You might discover that a 10% increase in your blog traffic has a much bigger effect on sign-ups than a 10% increase in your ad budget, telling you exactly where to focus your resources for the best return.

Making Smarter Business Decisions

The applications are endless. An e-commerce store can analyze how discounts and shipping costs impact the number of units sold. A restaurant manager could look at the relationship between local event attendance, weather, and nightly sales to optimize staffing levels. It helps you quantify gut feelings and replace guesswork with data.

Step 1: Enable the Analysis ToolPak in Excel

Before you can do any analysis, you need to make sure the right tool is enabled. Excel comes with a free, powerful add-in called the Analysis ToolPak, but it’s often turned off by default. Activating it is a one-time setup.

For Windows users:

Go to File > Options.
Click on Add-ins in the left-hand menu.
At the bottom of the window, you'll see a dropdown menu next to "Manage." Make sure it says Excel Add-ins and click Go....
In the new dialog box, check the box next to Analysis ToolPak and click OK.

For Mac users:

Click on Tools in the top menu bar.
Select Excel Add-ins....
In the dialog box, check the box next to Analysis ToolPak and click OK.

You’ll now have a "Data Analysis" button under the "Data" tab in your Excel ribbon. This is where you'll find the regression tool.

Step 2: Performing the Regression Analysis (A Walkthrough)

Let's walk through an example. Imagine you run an online store and want to understand how your monthly ad spend impacts your sales revenue. You've collected data for the last 12 months.

1. Set Up Your Data

Organize your data in two columns. It's best practice to put your Independent Variable (X) on the left and your Dependent Variable (Y) on the right. In our case, that would be "Ad Spend ($)" and "Sales Revenue ($)". It’s also helpful to include headers.

2. Open the Data Analysis Tool

Go to the Data tab and click on the Data Analysis button you just enabled. A new window will pop up with a list of different analysis tools.

Scroll through the list and select Regression, then click OK.

3. Configure the Regression Settings

The regression dialog box asks for a few inputs. Here’s what each means:

Input Y Range: This is for your dependent variable (the "effect"). Click the selector button and highlight your "Sales Revenue ($)" data, including the header.
Input X Range: This is for your independent variable (the "cause"). Click the selector button and highlight your "Ad Spend ($)" data, including the header.
Labels: Check this box. This tells Excel that the first row of your selection contains the column names ("Ad Spend" and "Sales Revenue") and not numerical data.
Confidence Level: Leave this at the default 95%. It's a standard statistical measure of reliability.
Output Options: The best choice here is New Worksheet Ply. This will generate the regression report on a fresh worksheet, keeping your original data clean. You can name the new sheet "Regression Summary" or something similar.
Residuals & Plots (Optional but good advice): It’s a great idea to check the Residuals and Line Fit Plot boxes. These will help you visualize the results and spot any issues with your analysis, which is helpful even for beginners.

4. Click OK and Get Your Results

After clicking OK, Excel will instantly create a new worksheet with your complete regression analysis output. At first, it might look like a wall of numbers, but we'll break down exactly what to look for next.

Step 3: How to Interpret the Results (Without a Statistics Degree)

Your results page is broken into a few sections. You can ignore most of it and focus on just a handful of figures that tell you almost everything you need to know.

Let's look at the key pieces of the output.

The "Regression Statistics" Table

This top table tells you how well your model fits the data - its overall accuracy and reliability.

R Square: This is arguably your most important takeaway. It tells you, as a percentage, how much of the change in your dependent variable (Sales) can be explained by your independent variable (Ad Spend). A value of 0.82 means that 82% of the variation in your sales can be explained by your ad spend. A higher R Square is generally better, signaling a stronger relationship.

The "Coefficients" Table - Where the Formula Lives

This is where you'll find the numbers to build your predictive formula. Your goal is to create an equation in this format: Y = mX + b.

Intercept Coefficient: This is your b value, or your baseline. It's the predicted value of your dependent variable if your independent variable was zero. In our sales example, this would mean: "If we spent $0 on ads, our model predicts we would still generate $[Intercept value] in sales" (perhaps from repeat customers or other channels).
X Variable Coefficient: This will be labeled with the name of your independent variable (e.g., "Ad Spend ($)"). This is your m value, representing the "slope." It’s the single most important part of the analysis. It tells you: "For every one-unit increase in X, how much does Y change?"

Putting It All Together: Your Prediction Formula

Let's say your output gives you an Intercept of 1200 and an "Ad Spend ($)" Coefficient of 3.5.

Your predictive formula would be:

Predicted Sales = (3.5 * Ad Spend) + 1,200

Now you can make some powerful forecasts. If you plan to spend $2,000 on ads next month, your predicted sales would be:

(3.5 * 2000) + 1,200 = $8,200

Just like that, you’ve moved from hoping to predicting.

What About the P-value?

You'll see a 'P-value' column in the Coefficients table. Here's a simple way to think about it: the P-value tells you if your results are statistically significant, or if they could have just happened by random chance.

A P-value less than 0.05 is considered good. It means there is less than a 5% chance that the relationship you found is a fluke. You can be confident that your independent variable truly has an impact on your dependent variable.
If the P-value for your variable is higher than 0.05, you should be skeptical of the results, as the connection is not statistically strong.

Leveling Up: Running a Multiple Regression in Excel

What if you believe your sales are driven by more than just ad spend? Maybe your website traffic also plays a major role. You can easily test this with a multiple regression analysis.

To do this, simply organize your data with all independent-variable columns next to each other (e.g., one column for Ad Spend and another for Website Traffic).

When you run the analysis in Excel, the process is nearly identical. The only change is that for the Input X Range, you will highlight all of your independent variable columns at once.

The output table will now give you a coefficient for each independent variable. This lets you see the individual impact of each factor while the others are held constant. For example, it might tell you that a $1 increase in ad spend leads to a $3 sales increase (holding traffic steady), while 100 extra website visitors lead to a $15 sales increase (holding ad spend steady). This helps you find the most efficient levers to pull in your business.

Final Thoughts

Regression analysis in Excel is a surprisingly accessible way to look under the hood of your business performance. Once you enable the Analysis ToolPak, you can move beyond just tracking what happened and start making data-driven predictions about what will happen next. It helps you quantify relationships, verify your instincts, and find the real drivers of your growth.

If you find yourself constantly exporting CSVs and digging into spreadsheets to run these analyses, you might find the process can still be time-consuming, especially when dealing with data from multiple platforms. We built Graphed to solve exactly this problem. You can connect all your sales and marketing data sources (like Google Analytics, Facebook Ads, Shopify, etc.), and then simply ask in plain English for the insight you need: "What is the correlation between my Google Ads clicks and my Shopify revenue last quarter?" We build the visuals and give you the answers in seconds, getting you straight to the strategic insights so you can spend less time wrangling data and more time growing your business.