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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, clickÂ **Add-ons > Get add-ons**:

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

Lastly, click the blue **Install** button.

**Step 2: Enter the Data**

Next, weâ€™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:

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

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