How to Do Regression Analysis in Google Sheets with ChatGPT

Trying to understand how your marketing budget impacts sales, or how website traffic influences sign-ups, doesn’t have to involve confusing formulas or a degree in statistics. Regression analysis, a powerful tool for finding these relationships, is now more accessible than ever right inside Google Sheets, thanks to ChatGPT. This guide will walk you through, step-by-step, how to use this combination to turn your spreadsheet data into clear, actionable insights.

What is Regression Analysis, Anyway?

At its core, regression analysis is a method used to understand the relationship between different variables. You're basically trying to see if changes in one thing (like your ad spend) predict or cause changes in another (like your monthly revenue).

Imagine you run an e-commerce store. You've been spending different amounts on Facebook Ads each month and have a record of your monthly sales. You have a nagging feeling that more ad spend leads to more sales, but you're not sure how strong that connection is. Regression analysis helps you measure that relationship and even predict future sales based on your planned ad budget.

There are two common types you'll likely encounter:

Simple Linear Regression: This is the most straightforward type. It looks at the relationship between one independent variable (the cause, e.g., Ad Spend) and one dependent variable (the effect, e.g., Sales).
Multiple Regression: This is a step up. It examines the relationship between two or more independent variables (e.g., Ad Spend and Website Traffic) and one dependent variable (e.g., Sales). This helps you see which factor has the biggest impact.

Why Use ChatGPT for Regression?

Google Sheets actually has built-in functions for regression, like LINEST, SLOPE, and INTERCEPT. But let's be honest - most of us don't remember what these functions are, how they work, or what all the numbers in the output mean. It’s a huge barrier for marketers, founders, and anyone who isn’t a dedicated data analyst.

This is where ChatGPT changes the game. It bridges the gap by translating your plain-English questions into statistical analysis. The benefits are clear:

No Formulas to Memorize: You don't need to learn specialized Google Sheets functions. Just describe what you want to find out.
Get Explanations, Not Just Numbers: ChatGPT doesn't just give you the results, it can explain what they mean for your business in simple terms.
Move Faster: It turns a process that could take an hour of Googling and troubleshooting into a two-minute conversation.

You can skip the steep learning curve of traditional data analysis tools and get straight to the insights that help you make better decisions.

Step 1: Get Your Data Ready in Google Sheets

Before you turn to AI, you need clean, well-organized data. A messy spreadsheet will lead to a confusing or incorrect analysis, no matter how smart the tool is. Your data should be simple and structured logically, with clear headers for each column.

Let's use a business example. We want to see how ad spend impacts sales. Here’s a sample dataset you can create in Google Sheets:

A few critical data prep tips:

Label Your Columns: Use clear, simple headers like "Month," "Ad Spend ($)," and "Sales ($)." This helps you (and ChatGPT) understand what each column represents.
Identify Your Variables: Know which variable you believe is causing the change (the independent variable, in this case, "Ad Spend") and which is being affected (the dependent variable, "Sales").
Clean Your Data: Check for any empty cells or text values in columns that should only contain numbers. Make sure currency symbols don't interfere with the data being read as numerical values.

Step 2: Using ChatGPT for the Analysis

Once your data is prepped, it's time for the analysis. The most common and direct way to do this is by copying your data from Google Sheets and pasting it directly into the ChatGPT interface. While there are Google Sheets add-ons, the copy-paste method is quick and universally accessible.

Crafting the Right Prompt

The magic is all in the prompt. You need to tell ChatGPT what data you have, what your variables are, and what you want it to do. Be as specific as possible for the best results.

Example Prompt for Simple Linear Regression

Start by highlighting your data in Google Sheets (including the headers) and copying it. Then, paste it into ChatGPT with a prompt like this:

Interpreting the Results

ChatGPT will process your data and give you an output. Now, you just need to understand what it means. Let’s break down the key pieces of its response:

The Regression Equation (y = mx + b): This is the formula that describes the relationship between your variables.
- The slope (m) tells you how much the dependent variable (Sales) is expected to change for every one-unit increase in the independent variable (Ad Spend). A slope of 9.9 means that for every additional $1 spent on ads, you can expect an increase of approximately $9.90 in sales.
- The intercept (b) is the predicted value of your sales if your ad spend were zero. In our example, an intercept of $1,559 suggests you would still make around that much in sales with no ad spending, perhaps from other channels or organic traffic.
R-squared (R²): This crucial metric, also called the coefficient of determination, tells you how well your model fits the data. It's a percentage that indicates how much of the variation in your sales can be explained by your ad spend.
- An R-squared of 0.96 (or 96%) is very high. It means that 96% of the changes in our monthly sales can be directly explained by the changes in our ad spend. This signals a very strong and reliable relationship.
P-value (often included): If you see a p-value, it's a measure of confidence. A small p-value (typically less than 0.05) means the relationship you've found is statistically significant and not just a random fluke.

ChatGPT’s plain-English explanation makes this an incredibly powerful tool. It transforms abstract numbers into a concrete business narrative: "Our data shows a strong, positive link between ad spend and sales. Every dollar we invest in advertising generates about $9.90 in revenue."

Step 3: Visualize Your Results in Google Sheets

A picture is worth a thousand words, especially in data analysis. While ChatGPT provides the numbers, creating a chart in Google Sheets helps you truly see the relationship.

A scatter plot with a trendline is the perfect visual for a regression analysis.

In Google Sheets, select the two columns of data you analyzed (Ad Spend and Sales).
Go to Insert > Chart. Google Sheets will likely suggest a scatter chart automatically.
In the Chart editor that appears on the right, go to the Customize tab.
Click on the Series dropdown menu.
Scroll down and check the box for Trendline. This line visually represents your regression equation.
Under the Trendline options, find the Label dropdown and select Use Equation. Then, check the box for Show R².

Now you have a professional-looking chart that perfectly complements your analysis, making it easy to share with your team or stakeholders.

Limitations and Best Practices

Using ChatGPT for this process is powerful, but it’s important to be aware of its limitations.

Data Privacy: Be cautious about pasting sensitive or personally identifiable information (PII) into any third-party AI tool. Use anonymized or non-sensitive data whenever possible.
Static Analysis: ChatGPT analyzes the data you paste at a single point in time. It is not connected live to your Google Sheet. If your data updates, you’ll need to repeat the copy-paste process.
Verification is Key: While highly accurate for this type of task, it's always good practice to sense-check the results. Does the relationship ChatGPT found align with your business intuition? If the results seem wildly off, double-check your data for errors.

Final Thoughts

Combining Google Sheets with ChatGPT makes regression analysis more approachable than ever before. You no longer need to be a spreadsheet expert to uncover the critical relationships hiding in your data. It allows anyone on your team to move from raw numbers to strategic insights with just a simple conversation.

For one-off analyses, copying and pasting data is a great solution. But once you start analyzing data from multiple sources like Google Analytics, Shopify, and your ad platforms, bouncing between tabs and preparing data becomes a full-time job. We created Graphed to solve this very problem by connecting directly to all your data sources. You can use natural language to ask questions and build real-time, shareable dashboards without ever having to manually export a CSV file again.