20 C
London
Monday, July 21, 2025
HomeSoftware TutorialsGoogle SheetsHow to Perform Logistic Regression in Google Sheets

How to Perform Logistic Regression in Google Sheets

Related stories

Learn About Opening an Automobile Repair Shop in India

Starting a car repair shop is quite a good...

Unlocking the Power: Embracing the Benefits of Tax-Free Investing

  Unlocking the Power: Embracing the Benefits of Tax-Free Investing For...

Income Splitting in Canada for 2023

  Income Splitting in Canada for 2023 The federal government’s expanded...

Can I Deduct Home Office Expenses on my Tax Return 2023?

Can I Deduct Home Office Expenses on my Tax...

Canadian Tax – Personal Tax Deadline 2022

  Canadian Tax – Personal Tax Deadline 2022 Resources and Tools...

Logistic regression is a method we can use to fit a regression model when the response variable is binary.

The following step-by-step example shows how to perform logistic regression in Google Sheets.

Step 1: Install the XLMiner Analysis ToolPak

To perform logistic regression in Google Sheets, we need to first install the free XLMiner Analysis Toolpak.

To do so, click Add-ons > Get add-ons:

Next, type XLMiner Analysis ToolPak in the search bar and click the icon that appears:

Install XLMiner analysis toolpak in Google Sheets

Lastly, click the blue Install button.

XLMiner Analysis Toolpak in Google Sheets

Step 2: Enter the Data

Next, we’ll enter the following data into Google Sheets:

We will fit a logistic regression model that uses points and assists to predict whether a basketball player gets drafted into the NBA (0 =No, 1 = Yes).

Step 3: Perform Logistic Regression

To fit the logistic regression model, click the Extensions tab, then click XL Miner Analysis ToolPak, then click Start:

In the panel that appears on the right side of the screen, click the dropdown arrow next to Logistic Regression and type in the following information:

logistic regression in Google Sheets

Once you click OK, the summary of the logistic regression model will be displayed:

logistic regression output in Google Sheets

The coefficients in the output indicate the average change in log odds of getting drafted.

For example, a one unit increase in points is associated with an average increase of 0.212 in the log odds of getting drafted.

The sign on the coefficients tells us whether there is a positive or negative associated between each predictor variable and the response variable.

For example, since points has a positive sign for the coefficient it means that increasing the value for points increases the chances that a player gets drafted (assuming assists is held constant).

Conversely, since assists has a negative sign for the coefficient it means that increasing the value for assists decreases the chances that a player gets drafted (assuming points is held constant).

The p-values in the output also give us an idea of how effective each predictor variable is at predicting the probability of getting drafted:

  • P-value for points: 0.02
  • P-value for assists: 0.35

We can see that points seems to be a statistically significant predictor variable since it has a p-value less than .05, but assists does not seem to be statistically significant since it does not have a p-value less than .05.

Additional Resources

The following tutorials explain how to perform other common tasks in Google Sheets:

How to Perform Polynomial Regression in Google Sheets
How to Perform Linear Regression in Google Sheets
How to Calculate R-Squared in Google Sheets

Subscribe

- Never miss a story with notifications

- Gain full access to our premium content

- Browse free from up to 5 devices at once

Latest stories