We can see that points seems to be a statistically significant predictor variable since it has a p-value less than.

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: The sign on the coefficients tells us whether there is a positive or negative associated between each predictor variable and the response variable.įor 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).Ĭonversely, 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 coefficients in the output indicate the average change in log odds of getting drafted.įor example, a one unit increase in points is associated with an average increase of 0.212 in the log odds of getting drafted. Once you click OK, the summary of the logistic regression model will be displayed: 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: To fit the logistic regression model, click the Extensions tab, then click XL Miner Analysis ToolPak, then click Start: 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). Next, we’ll enter the following data into Google Sheets: Next, type XLMiner Analysis ToolPak in the search bar and click the icon that appears: To perform logistic regression in Google Sheets, we need to first install the free XLMiner Analysis Toolpak. Step 1: Install the XLMiner Analysis ToolPak The following step-by-step example shows how to perform logistic regression in Google Sheets. Logistic regression is a method we can use to fit a regression model when the response variable is binary.

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