Linear regression is one of the simplest and most useful tools for analyzing data. It helps you find the relationship between variables so you can make predictions and understand patterns. In this guide, we’ll show you how to perform both simple and multiple linear regression in Google Sheets using the built-in LINEST function.
We’ll also explain why Sheets is a handy choice for this type of analysis and look at some other tools you might consider.
What Is Linear Regression?
Linear regression is a statistical method used to model the relationship between two or more variables. It helps answer questions like, “If I study more hours, will my test score improve?” or “Can I predict car prices based on their age and mileage?” In this way, it’s a good partner for trend lines in Google Sheets.
In simple terms:
- Simple linear regression uses one independent variable to predict one dependent variable.
- Multiple linear regression uses two or more independent variables to predict a dependent variable.
The result is an equation you can use to estimate future outcomes based on known data.
Google Sheets is a solid tool for quick, lightweight data analysis. It’s especially useful for doing quick calculations without having to install any software. It lives online, and that makes it easy for anyone, even those with older computers to perform complex calculations.
It’s also ideal for sharing in real time and visualizing data alongside an analysis. Plus you can run a basic regression model without having to learn Python.
For anyone working with small to medium-sized datasets, Google Sheets offers everything you need to get started.
How to Do Linear Regression in Google Sheets
You’ll use the LINEST function to perform linear regression. It works for both simple and multiple regression.
=LINEST(known_data_y, [known_data_x], [calculate_b], [verbose])
- known_data_y (required): Your dependent variable (the one you’re predicting)
- known_data_x (optional): Your independent variable(s)
- calculate_b (optional): Whether to calculate the y-intercept (default is TRUE)
- verbose (optional): Whether to return additional regression statistics (default is FALSE)
Let’s say you want to understand how a car’s age affects its resale price. You’ve collected data showing the age of different cars alongside their selling prices. Once your data is in place, with car age in column A and car price in column B, you’re ready to run the regression.
To start, select a block of empty cells large enough to display the regression output. If you want to include additional regression statistics like R², standard errors, and F-statistics, choose a 2-column by 5-row block. Then type the following linear regression equasion:
=LINEST(B2:B12, A2:A12, TRUE, TRUE)This function will calculate the slope of the regression line, the y-intercept, and several other statistics. These values describe the trend between car age and price, allowing you to create a prediction equation. In this example, you’re using simple linear regression, since you’re working with just one independent variable: car age.
For this Step-by-Step guide below, I will use the following table that contains data with a linear trend to explain how to do linear regression in Google Sheets using the LINEST function. And you’ll notice the syntax above mentioned throughout.
This table shows how the age of a car affects its price, and these two variables will be used in the simple linear regression example. In addition, this table also shows how the age of this car affects its maintenance cost; we will use this variable and the price in the multiple regression example.
So, let’s see how they work!
Step-By-Step Guide for Linear Regression
Step 1: Add a table for the different values that will be generated using the =LINEST function.
Step 2: Use the function LINEST in Google Sheets to generate regression statistics.
In the screenshot above, I have used the function:
=LINEST(A2:A12,B2:B12,TRUE,TRUE)
I use this to find the regression statistics for the variables Car Age and Car Price.
And we can get even more creative with this.
Multiple Regression in Google Sheets
If we want to do multiple regression in Google Sheets, we can just continue with the following steps.
Step 1: Add another column to the right of column G.
Step 2: Change the cell range for Known_data_x from B2:B12 to B2:C12 in order to include the Maintenance Cost variable.
And done! You’ve successfully generated Google Sheets regression statistics using Simple Linear regression and Multiple Regression methods. You can now use these to chart a line or curve for visualization.
Other Ways to Run Linear Regression
While Google Sheets is a quick and effective way to run linear regression, it’s not the only option. Microsoft Excel includes a Data Analysis Toolpak that lets you run regression models and generate full reports, including ANOVA tables and residual plots. It’s a bit more robust for people already using Excel for larger datasets.
Python is another popular choice, especially for those who want to automate or scale their analyses. Libraries like scikit-learn and statsmodels allow you to build detailed regression models with just a few lines of code. Python is ideal for data scientists, analysts, and developers working with large or dynamic datasets.
R is a programming language built for statistics. It offers comprehensive tools for regression modeling and is widely used in research and academia. It’s a great fit for anyone needing detailed diagnostics and publication-quality output.
Finally, if you just want to plug in some values and get a regression line quickly, there are free online calculators such as Desmos and GraphPad. These tools are user-friendly and perfect for visual learners or those doing quick checks without setting up a full spreadsheet.
Google Sheets is a great starting point. It offers a simple and accessible way to understand how regression works. But if your analysis needs grow more complex, there are plenty of other tools that can take you further.
Understanding Linear Regression Analysis
Linear regression analysis goes beyond just drawing a line through data points. It involves evaluating how well the model explains the relationship between variables and how confident you can be in using it to make predictions. This includes interpreting the slope and y-intercept, assessing the model’s fit, and reviewing related statistics like the coefficient of determination, or R².
The coefficient of determination, written as R², is a key output of regression analysis. It tells you how much of the variation in the dependent variable (what you’re trying to predict) can be explained by the independent variable or variables (the predictors).
An R² value of 0.85, for example, means that 85 percent of the variation in the outcome can be explained by your model. The closer R² is to 1, the stronger the explanatory power of your regression.
Explanatory variables, also called independent variables, are the inputs you use to predict the outcome. In simple regression, you have just one. In multiple regression, you might have several, and understanding which ones contribute most to prediction is part of the analysis.
A well-structured linear regression not only gives you a usable equation. It helps reveal which variables truly matter and how they interact.
Linear regression is a valuable tool for analyzing how one or more variables affect an outcome, such as how advertising spend impacts sales. In Google Sheets, the LINEST function makes it easy to run both simple and multiple linear regression without installing anything.
It takes parameters like known_data_y (your response variable) and known_data_x (your explanatory variables), and returns an array of values that include the slope, y-intercept, and optional regression statistics. This is especially useful for common business tasks like forecasting sales data or understanding the effect of marketing efforts.
Whether you’re working with a single independent variable or a full advertising campaign across channels, Google Sheets can quickly generate usable equations and insights to guide your decisions.
