How to Do Regression Analysis in Excel with AI
Running a regression analysis in Excel can feel like you need a statistics degree just to get started. You’re ready to find the hidden relationships in your data - like how your ad spend impacts sales or how website visits influence sign-ups - but you’re stopped by confusing dialog boxes and a wall of statistical output. This article will walk you through how to perform regression analysis, first using Excel's built-in tools and then by exploring how AI can make the entire process significantly easier.
What Exactly Is Regression Analysis?
Let's forget the textbook definitions for a moment. At its core, regression analysis is about understanding and measuring the relationship between two or more variables. Think of it as a way to find a predictable pattern in your data.
For example, as an ice cream shop owner, you probably notice you sell more ice cream on hotter days. Regression analysis helps you quantify that relationship. It can help you answer questions like, "For every one-degree increase in temperature, how many more ice cream cones can I expect to sell?"
In this analysis, there are two key types of variables:
Dependent Variable: This is the main thing you're trying to predict or explain. In our example, it’s ice cream sales. It’s "dependent" because we think its value depends on something else.
Independent Variable(s): These are the factors you believe influence your dependent variable. In this case, the temperature is the independent variable.
You can have one independent variable (like temperature affecting sales), which is called simple linear regression. Or, you can have multiple independent variables (like temperature, day of the week, and local events all affecting sales), which is called multiple linear regression.
The Classic Method: How to Run a Regression in Excel
Before AI copilots, this was the go-to method for analysts. It's powerful, but it involves a few manual steps and requires you to interpret the statistical output yourself. To do this, you'll need to use Excel’s “Data Analysis ToolPak.”
Step 1: Make Sure the Data Analysis ToolPak is Enabled
This add-in isn't turned on by default, so you might need to enable it first. Here’s how:
Click on File in the top-left corner, then go to Options at the bottom of the left-hand menu.
In the Excel Options pop-up window, click on Add-ins.
At the bottom of the window, next to "Manage," make sure Excel Add-ins is selected and click Go...
In the new pop-up, 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.
Step 2: Get Your Data Ready
Regression analysis works best with clean, organized data arranged in columns. Let's imagine you're a marketer trying to understand the relationship between your monthly Facebook Ad Spend and the resulting Website Sales. Your data in Excel might look something like this:
Column A: Month (Jan, Feb, Mar...)Column B: Facebook Ad Spend ($)Column C: Website Sales ($)
Here, Website Sales is your dependent variable (what you want to predict), and Facebook Ad Spend is your independent variable (the factor you think is influencing sales).
Step 3: Run the Analysis
With your data in place and the ToolPak enabled, you're ready to go.
Go to the Data tab and click on Data Analysis.
Scroll through the list and select Regression, then click OK.
A Regression dialog box will appear. This is where you tell Excel what to analyze:
Input Y Range: Click in this box and select your dependent variable data. For our example, this is the range of cells containing your Website Sales data (e.g., C2:C13). "Y" always represents the dependent variable.
Input X Range: Now, do the same for your independent variable. Select the range of cells containing your Facebook Ad Spend data (e.g., B2:B13). "X" always represents the independent variable.
Labels: If you included the column headers ("Facebook Ad Spend" and "Website Sales") in your selection, check this box. It helps make the output report easier to read.
Output Range: Choose where you want Excel to place the results. You can select a cell on the same sheet, a new sheet, or a new workbook.
Click OK.
Excel will instantly generate a “Summary Output” table filled with numbers and statistical terms. This is where many people get intimidated.
Step 4: Understand the Results (Without a PhD)
The output table is dense, but you only need to focus on a few key numbers to get the core insights.
R Square: Found in the "Regression Statistics" section, this tells you how well your independent variable explains the change in your dependent variable. The value ranges from 0 to 1 (or 0% to 100%). An R Square of 0.85 means that 85% of the variation in your website sales can be explained by your Facebook ad spend. A higher R Square is generally better.
Coefficients: This is where you find the actual formula for your relationship. Look for two numbers:
Intercept: This is your baseline. It's the predicted value of your sales if you spent $0 on ads.
Coefficient for your Variable (e.g., "Facebook Ad Spend"): This is the most important number. It tells you how much your dependent variable (sales) is expected to increase for every one-unit increase in your independent variable (ad spend). If this coefficient is 4.5, it means that for every additional $1 you spend on Facebook ads, you can expect an additional $4.50 in sales.
So, your prediction formula becomes: Predicted Sales = Intercept + (4.5 * Your Ad Spend).
P-value: This number tells you if your results are "statistically significant" - in non-statistician terms, it answers the question, "Is this relationship real, or could it have happened by random chance?" A P-value lower than 0.05 is the accepted standard. If the P-value for your ad spend is very small (e.g., 0.001), you can be confident that there's a real, meaningful relationship between your ad spend and sales.
The New Wave: Letting AI Handle Your Regression Analysis
The manual method in Excel works, but it's rigid. It requires data prep, menu clicking, and self-interpretation. AI-powered analytics tools change this process from a chore into a conversation, letting you focus on the insights, not the statistical mechanics.
How AI Simplifies the Entire Workflow
The biggest shift is moving from pulling levers to simply asking questions. You don't need to manually prep a CSV file, learn which variable is X or Y, or decipher P-values. AI handles the technical "how" so you can focus on the business "what."
Modern AI analytics platforms streamline this into a few simple steps:
Connect Your Data Directly: Instead of downloading CSVs from all your platforms and consolidating them in Excel, AI tools connect directly to the source. You can link your Google Analytics, Shopify, Facebook Ads, HubSpot, and Salesforce accounts once, and the data flows in automatically and stays up-to-date. No more weekly data dumps.
Ask in Plain English: Once connected, you can just ask what you want to know. Instead of setting up the regression tool, you write a conversational prompt:
"Show me the relationship between my spend on Facebook Ads and my Shopify revenue for the last quarter."
"What is the correlation between new users from Google Analytics and MQLs in HubSpot?"
"Create a regression chart that predicts sales based on website traffic."
Get an Instant Visualization and an Explanation: The AI doesn't just return a sterile table. It generates the chart for you - usually a scatter plot with the regression line cleanly drawn. More importantly, it explains the result in plain English. Instead of you interpreting the R-squared and coefficients, the AI might tell you: "There is a strong, positive relationship between your ad spend and revenue. On average, for every $1 increase in ad spend, your revenue increases by $5.20. This model explains 92% of the variation in revenue."
Ask Follow-Up Questions: This is a massive advantage. Your analysis becomes an interactive conversation. After the initial result, you can dig deeper:
"Okay, break that down by campaign."
"Which ad creative has the most impact?"
"Based on this trend, what would our projected revenue be next month if we increased our budget by 25%?"
This transforms the process from a static report into a dynamic exploration. You can follow your curiosity, uncover a new insight, and immediately ask another question without ever leaving the dashboard or re-running a complex analysis.
Is Excel's Built-in AI Enough?
Excel does have its own AI features like "Analyze Data" (formerly Ideas) and the new Copilot integration. These can be useful for quickly spotting trends within a dataset that's already in your spreadsheet. For simple regressions from a single, clean CSV file, they might do the trick.
However, the real challenge for most marketing and sales teams isn’t running the analysis itself, it's getting all the reliable, up-to-date data into one place. This is where dedicated AI analytics tools have a significant edge. They function not just as an analysis engine but as a live data hub that understands the unique structures of platforms like Shopify (orders, products) and Salesforce (leads, deals). This deeper, contextual understanding of the source data leads to far more accurate and reliable analysis compared to an AI that's just looking at a generic table of numbers in a CSV.
Final Thoughts
Understanding the drivers of your business performance through regression analysis is one of the most powerful things you can do with your data. The traditional Excel method is perfectly capable, but it requires statistical legwork. AI removes this friction, transforming a time-consuming statistical procedure into a simple, natural language conversation with your data.
At our company, we built Graphed to solve this exact problem. We wanted to make it incredibly easy for anyone - not just data scientists - to perform sophisticated analyses. You simply connect your data sources like Google Analytics, Shopify, and Facebook Ads, and then ask questions like, "Show me how my ad spend impacts revenue." We instantly build the visualization, run the regression in the background, and give you a clear, understandable answer. It's about getting straight to the insights that help you make better decisions, without ever having to worry about P-values or navigating clunky menus.