How to Do Regression Analysis in Google Sheets

Trying to find the relationship between two business metrics, like how your ad spend affects sales or if more website traffic leads to more signups, is a core part of growing any business. This is exactly what regression analysis does: it helps you measure the connection between different variables so you can make smarter, data-backed predictions. This article will walk you through, step-by-step, how to perform a simple regression analysis right inside Google Sheets, no advanced statistics degree required.

What Is Regression Analysis Anyway? (And Why You Should Care)

At its heart, regression analysis is a way to model the relationship between variables. You're looking for a cause-and-effect pattern in your data. It helps you answer practical business questions like:

• "If we increase our Facebook Ads budget by $500 next month, how much extra revenue can we expect?"
• "Is there a strong connection between the number of sales calls my team makes and the number of deals they close?"
• "How does adding more subscribers to our email list impact our website's session duration?"

To do this, we need to define two types of variables:

• The Dependent Variable: This is the main outcome or result you are trying to understand or predict. Think of it as the effect. In the examples above, that would be revenue, closed deals, and session duration.
• The Independent Variable: This is the factor you believe has an influence on your dependent variable. Think of it as the cause. That would be the ad budget, number of sales calls, and email subscribers.

Essentially, you're trying to see if changes in your independent variable can reliably predict changes in your dependent variable. The end result is often a simple formula and a chart that shows you not just if a relationship exists, but how strong it is.

Setting Up Your Data in Google Sheets

Before you can do any analysis, your data needs to be clean and structured properly. For a simple linear regression, all you need are two columns in Google Sheets.

Your data should be organized with your independent variable in the first column and your dependent variable in the second. Each row should represent a specific time period or instance (e.g., a day, week, or month).

Let's use an example. Imagine we want to understand the relationship between our monthly advertising spend and the number of website sessions we get. Our data in Google Sheets would look something like this:

Example Dataset: Monthly Ad Spend vs. Website Sessions

For this example, let's put 'Monthly Ad Spend ($)' in Column A (our independent variable) and 'Website Sessions' in Column B (our dependent variable).

With our data correctly formatted, we’re ready for the analysis. We'll explore two great methods in Google Sheets.

Method 1: The Visual Approach with a Scatter Plot & Trendline

The fastest and most intuitive way to perform regression analysis in Google Sheets is by creating a scatter chart. It gives you a visual representation of the relationship and all the key numbers you need with just a few clicks.

Step 1: Create a Scatter Chart

Highlight your two columns of data, including the headers. Go to the menu and click Insert > Chart. Google Sheets will likely create a line chart by default. In the Chart editor panel on the right, under the 'Setup' tab, change the 'Chart type' to Scatter chart.

You'll immediately see your data points plotted. Each point represents one month, showing the ad spend on the x-axis and website sessions on the y-axis. Just by looking at the chart, you can probably get a feel for the relationship. If the dots trend upwards from left to right, it's a positive relationship (as ad spend increases, sessions increase).

Step 2: Add a Trendline and View the Equation

Now, let's add the regression line.

1. In the Chart editor, click on the Customize tab.
2. Click on the Series dropdown menu.
3. Scroll down and check the box for Trendline. A straight line will appear on your chart that best fits the data points.
4. To get the crucial analysis info, scroll down a bit further to the Label dropdown and select Use Equation.
5. Finally, check the box below that says Show R².

Your chart will now display an equation and an "R²" value. These are the outputs of your regression analysis!

Step 3: Understand the Results

Let's break down what that technical-looking label on your chart means. Using our sample data, the equation should be something like y = 6.48 * x + 5394.1.

• The Trendline: The line cutting through your data points shows the general direction and strength of the relationship.
• The Equation (y = mx + b): This is the formula for your trendline.
• R² (R-squared): This number tells you how well your independent variable explains the changes in your dependent variable. Our R² value is 0.96. You can think of this as a percentage (96%). This means that 96% of the variation in website sessions from month to month can be explained by the changes in our ad spend. An R² value closer to 1 indicates a stronger relationship. A value of 0.96 is very strong!

Method 2: Using the LINEST Function for More Statistical Detail

If you prefer to get straight to the numbers without a chart, or if you need additional statistical details, the LINEST (an abbreviation for "linear estimation") function is an incredibly powerful tool.

Step 1: Use the LINEST Formula

Find an empty area in your spreadsheet. The LINEST function outputs an array of data, so make sure you have a few empty cells below and to the right.

Click on an empty cell and type the following formula:

=LINEST(B2:B13, A2:A13, TRUE, TRUE)

Let's quickly break this down:

• LINEST() This is the name of the function.
• B2:B13 This is your range of known Y's (the dependent variable, Website Sessions).
• A2:A13 This is your range of known X's (the independent variable, Ad Spend).
• TRUE This tells the function that we want it to calculate the intercept normally (not force it to zero).
• TRUE This tells the function to return additional regression statistics.

Press Enter, and Google Sheets will populate a small table of values.

Step 2: Understand the LINEST Output

The output can look confusing at first, but it’s organized logically. Here's what the most important values represent:

• The top-left cell (6.48) is your slope, same as in the chart.
• The top-right cell (5394.1) is your intercept, also the same.
• The cell directly below the R-squared value in the third row, on the left (0.96), is your R-squared value. It matches perfectly with what our chart showed us.

While the other values are more for advanced statistical analysis, these three give you the core components you need, all generated from one single formula.

How to Interpret and Use Your Results for Business Growth

Getting the numbers is one thing, using them to make better decisions is where the real value lies.

1. Forecasting Future Performance

You can use the equation y = 6.48x + 5394.1 for forecasting. Suppose you’re planning your budget and want to estimate the traffic you'd get from spending $3,000 on ads next month.

You can plug that number in for 'x':

Website Sessions = (6.48 * 3000) + 5394.1

This gives us: 19440 + 5394.1 = 24,834.1

Our model predicts we would receive around 24,834 sessions if we spent $3,000 on ads. This helps tremendously with goal setting and budget allocation.

2. Quantifying Your Marketing ROI

The slope itself is a powerful insight. A slope of 6.48 tells a story. It quantifies the direct impact of your investment. You now have a benchmark - every ad dollar "buys" us about 6.5 sessions. You can compare this to other channels. Could $1 spent on email marketing or content creation generate more? This analysis gives you the data to find out.

3. A Few Important Cautions

• Correlation isn't causation. Our analysis shows a very strong relationship between ad spend and traffic, but it doesn't prove the ads are the only cause. An external factor, like seasonality, could be influencing both. However, it's a very strong indicator.
• Be careful extrapolating. The model is based on data between $500 and $2,500 in ad spend. Using it to predict traffic from a $20,000 budget might not be accurate, as the relationship might change at a larger scale. Stick to predictions within or near your actual data range.

Final Thoughts

Running a regression analysis in Google Sheets using a scatter plot or the LINEST function is an incredibly effective way to turn simple data into powerful business forecasts. It moves you from just guessing about what strategies work to actually quantifying their impact, allowing you to allocate resources more intelligently and set realistic goals for growth.

While Google Sheets is a great tool for this, the process still involves several manual steps, from setting up data correctly to creating charts and interpreting formulas. If you want to get these kinds of insights instantly, that's precisely what we built Graphed for. We connect directly to your data sources like Google Analytics, Shopify, and various ad platforms. You can simply ask, "What is the relationship between my ad spend and revenue?" and get a complete analysis with visualizations and plain-English explanations in seconds, no formulas or chart editors needed.