how to do linear regression in google sheets

Daniel Parker

3 min read

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06-02-2025

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Linear regression is a powerful statistical method used to model the relationship between a dependent variable and one or more independent variables. It's particularly useful for predicting future outcomes based on existing data. Fortunately, you don't need specialized software; Google Sheets provides the tools to perform linear regression analysis directly within its interface. This guide will walk you through the process.

Understanding Linear Regression

Before diving into the Google Sheets implementation, let's briefly define linear regression. The goal is to find the best-fitting straight line (or hyperplane in multiple regression) that represents the relationship between your variables. This line is defined by an equation of the form:

• y = mx + c

Where:

• y is the dependent variable (what you're trying to predict).
• x is the independent variable (the predictor).
• m is the slope of the line (representing the change in y for a unit change in x).
• c is the y-intercept (the value of y when x is 0).

Performing Linear Regression in Google Sheets

Google Sheets offers the LINEST function to calculate the coefficients of the linear regression equation. This function is incredibly versatile and can handle both single and multiple linear regression.

1. Prepare Your Data:

• Organize your data in a spreadsheet. The independent variable(s) should be in one or more columns, and the dependent variable in another.

Example: Let's say you're trying to predict house prices (dependent variable) based on their size (independent variable). Your data might look like this:

2. Using the LINEST Function:

The LINEST function returns an array of values. To use it effectively, select a range of cells (at least two columns wide and as many rows as there are independent variables plus one) and then enter the formula.

The basic syntax is:

LINEST(known_y's, [known_x's], [const], [stats])

• known_y's: The range of cells containing your dependent variable (House Price in our example).
• known_x's: (Optional) The range of cells containing your independent variable(s). Omit this if you're doing simple linear regression with only one independent variable.
• const: (Optional) A logical value specifying whether to force the intercept (c) to be zero. TRUE (default) includes the intercept; FALSE forces it to zero.
• stats: (Optional) A logical value specifying whether to return additional regression statistics. TRUE returns these statistics; FALSE (default) only returns the coefficients.

Example (Simple Linear Regression):

Let's assume your house sizes are in column A (A2:A6) and house prices are in column B (B2:B6). To perform simple linear regression, select a 2x1 range of cells (e.g., D2:E2), and enter the following formula:

=LINEST(B2:B6, A2:A6, TRUE, TRUE)

Press Ctrl + Shift + Enter (Windows) or Cmd + Shift + Enter (Mac) to enter the formula as an array formula. This will populate the selected cells with the following:

• Cell D2: The slope (m) of the regression line.
• Cell E2: The y-intercept (c) of the regression line.

Below these cells (e.g., D3 onwards), additional regression statistics will be displayed if you included TRUE for the stats argument. These include R-squared, standard error, F statistic, and more, providing a comprehensive analysis.

Example (Multiple Linear Regression):

If you had multiple independent variables (e.g., house size, number of bedrooms, location score), you would adjust the known_x's argument accordingly, specifying the ranges for each independent variable. The resulting array would contain the coefficients for each variable.

3. Interpreting the Results:

Once you have the slope (m) and intercept (c), you can construct your regression equation. Using our example:

• Suppose LINEST returns a slope of 150 and an intercept of 50000.
• Your equation would be: House Price = 150 * House Size + 50000

This equation allows you to predict the house price based on its size.

Visualizing the Regression Line (Optional)

To visually represent the regression line, you can create a scatter plot of your data and add a trendline.

1. Select your data (both independent and dependent variables).
2. Insert a chart (Insert > Chart). Choose a scatter chart.
3. Customize the chart: Click on the chart, then go to "Customize" > "Series".
4. Check the "Trendline" box. Choose "Use Equation" to display the equation on the chart.

Conclusion

Linear regression in Google Sheets is a straightforward process that provides valuable insights into the relationships between variables. By understanding the LINEST function and its various arguments, you can easily perform both simple and multiple linear regressions and use the results for prediction and analysis. Remember to interpret the results carefully and consider the limitations of linear regression.