How to Do Regression Analysis in Google Sheets with AI

Thinking about how your marketing spend affects sales, or how website traffic influences sign-ups? That’s regression analysis, a powerful statistical method for uncovering the relationships hidden in your data. In this tutorial, we'll walk through how to perform a regression analysis in Google Sheets, from the classic built-in tools to game-changing AI methods that let you get insights without memorizing a single formula.

What is Regression Analysis, Really?

At its heart, regression analysis helps you understand how one primary variable (the "dependent" variable) changes when you adjust one or more other variables (the "independent" variables). It’s a way to model the relationship between these variables to make predictions and better-informed decisions.

Let's make this real. Imagine you run an e-commerce store. You want to know:

“If I spend more on social media ads, how much will my sales increase?”

In this scenario:

• Dependent Variable: Monthly Sales (this is what you want to predict).

• Independent Variable: Monthly Ad Spend (this is the factor you believe influences sales).

Regression analysis creates a mathematical equation that describes this relationship. It can tell you, with a degree of confidence, that for every extra $100 you spend on ads, your sales might increase by, say, $500. It’s not about absolute certainty, it’s about understanding trends and making data-backed forecasts.

Businesses use regression analysis for things like:

• Budgeting & Forecasting: Predicting future revenue based on planned marketing spend.

• Optimization: Figuring out which marketing channel gives you the best return on investment.

• Understanding Customer Behavior: Analyzing how factors like price, promotions, or seasonality impact purchase behavior.

Step 1: Get Your Data in Order

Before you can analyze anything, your data needs to be clean and organized. Garbage in, garbage out is the first rule of data analysis. For regression, Google Sheets needs your data structured in clean columns where each row represents a single time period or observation.

Continuing our e-commerce example, your sheet should look something like this:

Ensure that your data columns contain only numeric values and that there are no empty cells in the range you're analyzing.

Traditional Regression in Google Sheets: Two Methods

Before AI add-ons, you had two main ways to run a regression in Google Sheets. It's helpful to understand these methods to appreciate just how much easier AI makes the process.

Method 1: Visual Regression with a Chart Trendline

The quickest way to see a relationship is to visualize it. A scatter plot is perfect for this. It shows you the relationship between two variables at a glance.

1. Select your data: Highlight the two columns you want to compare. In our case, that would be 'Ad Spend' (the independent variable, usually on the X-axis) and 'Sales' (the dependent variable, on the Y-axis).

2. Insert a chart: Go to Insert > Chart. Google Sheets will often default to a scatter plot, but if not, select 'Scatter chart' from the Chart editor panel on the right.

3. Add the trendline: In the Chart editor, go to the Customize tab and scroll down to Series. Check the box for 'Trendline'. This line is your regression line - it’s the line that best fits the data points.

4. Show the equation and R-squared: Scroll down further within the Trendline options. Under 'Label', select 'Use Equation'. Then, check the box for 'Show R² value'.

You’ll now see an equation on your chart, something like y = 12.5x + 2500, and an R² value. This is a simple linear regression model right on your chart!

Method 2: Deeper Dive with the LINEST Function

For more detailed statistics, you can use the LINEST function (which stands for "linear estimation"). It's more powerful than the chart trendline but requires a bit more comfort with spreadsheet formulas.

The basic syntax is:

=LINEST(known_data_y, known_data_x)

• known_data_y: The range of your dependent variable (e.g., your 'Sales' column).

• known_data_x: The range of your independent variable (e.g., your 'Ad Spend' column).

To use it, select a 2x2 grid of empty cells, type the formula with your data ranges, and press Ctrl+Shift+Enter (on Windows) or Cmd+Shift+Enter (on Mac) to output an array of statistics.

The output gives you:

• The slope (m) and Y-intercept (b), which are the coefficients for your regression equation (y=mx+b).

• The R-squared value and other statistical metrics that help you determine the model's reliability.

This method works well, but you have to know what you’re doing, remember the formula, and understand how to interpret the array of numbers it gives you. For many people, this is where they start to get lost.

Enter AI: Regression Analysis Without the Formulas

This is where things get much simpler and more powerful. AI-powered Google Sheets add-ons change the entire workflow. Instead of clicking through menus or building formulas, you simply ask for what you want in plain English.

The core benefit of using AI is that it removes the technical barriers. You don’t need to be a data analyst or learn statistics to start uncovering insights. You can stay focused on the business questions you're trying to answer.

How AI Add-ons Streamline Regression Analysis

The process generally involves installing an add-on from the Google Workspace Marketplace, giving it access to your active sheet, and then using a conversational chat interface to make your request.

Instead of manually creating a chart, you can just prompt the AI:

“Create a scatter plot showing the relationship between Ad Spend and Sales, and add a regression line and the R squared value.”

The AI will generate the chart for you. But you can go much deeper.

You can also ask direct analytical questions, such as:

• "Run a regression analysis to predict Sales based on Ad Spend and Website Sessions." (This is a multiple regression, which is more complex to do manually).

• "What is the expected increase in Sales for every additional $1,000 in ad spend?"

• "Based on this data, predict our sales for next month if we spend $2,500 on ads."

The AI handles the statistical calculations behind the scenes and gives you the answer directly, often with a helpful explanation. This approach turns analysis from a technical task into a simple conversation with your data.

Making Sense of the Results

Whether you use the old method or an AI tool, getting the regression output is only half the battle. You need to understand what it means. Let’s break down the key pieces of information.

• The Equation (y = mx + b): This is the core of your model.

  • 'y' is what you want to predict (Sales).

  • 'm' is the slope. It tells you how much 'y' changes for a one-unit increase in 'x'. For example, if m=12.5, it means for every additional $1 in ad spend, sales increase by $12.50.

  • 'x' is your independent variable (Ad Spend).

  • 'b' is the y-intercept. It's the predicted value of 'y' when 'x' is zero. In this context, it's your baseline sales with zero ad spend.

• R-squared (R²): This value tells you how much of the variation in your dependent variable (Sales) can be explained by your independent variable (Ad Spend). It’s a value between 0 and 1. An R² of 0.75 means that 75% of the movement in your sales numbers can be explained by your ad spend. A higher R² generally indicates a better-fitting model, but "good" depends on your industry.

Best Practices to Get Reliable Results

To avoid common mistakes and ensure your analysis is sound, keep these tips in mind:

1. Ask a Clear Question: Before you even open your spreadsheet, know what you’re trying to understand. A specific question like "How does the number of website sessions impact our free trial sign-ups?" will guide your analysis much better than a vague one.

2. Use Sufficient Data: Regression analysis with only four or five data points is unlikely to be reliable. The more data you have (e.g., 20-30 periods or more), the more confident you can be in the trends you uncover.

3. Be Wary of Causation: This is a classic mantra in data analysis: correlation does not equal causation. Just because your ad spend and sales move together doesn't mean one is definitively causing the other. There could be another factor at play (like seasonality) that influences both. Regression shows a relationship, but you need business context to determine causality.

4. Test Different Variables: Start simple with one independent variable (simple linear regression). Then, consider adding another. For example, you could test how both Ad Spend and Website Sessions affect Sales. This is called multiple regression and can give you a much more nuanced view.

Final Thoughts

Regression analysis is an incredibly useful tool for finding actionable insights in your business data. While it used to be reserved for those with a statistics background, Google Sheets has made the essential functions accessible. With the addition of AI, this powerful analysis is now available for anyone on your team who can ask a question in plain English.

Here at Graphed, we’ve built on this very idea. A Google Sheet is a great starting point, but most marketing and sales data doesn't live there - it's scattered across Google Analytics, Shopify, Facebook Ads, Salesforce, and a dozen other platforms. We connect all those live data sources directly, so you don't even need to download a CSV. You can ask questions in natural language like, "Show me how my Google Ads cost-per-click impacts Shopify revenue," and get back a live, interactive dashboard instantly. It’s all about getting you from question to insight in seconds, not hours.