Section 9.2 Learning Objectives
By the end of this chapter, you will be able to:
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Explain when logistic regression is appropriate and how it differs from linear regression
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Interpret probabilities, odds, log-odds, and odds ratios in the context of binary outcomes
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Perform exploratory data analysis for a binary response variable, including calculating proportions and odds
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Visualize relationships between a binary outcome and predictor variables
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Split data into training and testing sets for predictive modeling
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Fit a logistic regression model using
glm()with a binomial family -
Interpret logistic regression coefficients and convert them to odds ratios using
exp() -
Assess model fit using McFadden’s pseudo-R²
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Evaluate predictor importance and check for multicollinearity using VIF
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Generate predicted probabilities and classifications from a fitted model
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Evaluate classification performance using confusion matrices, sensitivity, specificity, and balanced accuracy
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Assess model discrimination using ROC curves and the Area Under the Curve (AUC)
