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Master CDA and GLM applications
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Module 8
Binary Logistic Regression
1
Binary Logistic Regression: Model Formulation and Logit Interpretation
Formulate a binary logistic regression model and explain the interpretation of the logit link function.
2
Interpreting Logistic Regression Coefficients
Interpret logistic regression coefficients as log-odds and convert them to odds ratios with confidence intervals.
3
Assessing Logistic Regression Fit: Deviance and Hosmer-Lemeshow
Assess the goodness of fit of a logistic regression model using deviance-based statistics and the Hosmer-Lemeshow test.
4
Understanding Confusion Matrices and Performance Metrics
Define a confusion matrix and calculate performance metrics like sensitivity and specificity.
5
ROC Curves and AUC for Model Evaluation
Construct a ROC curve and use the Area Under the Curve (AUC) to evaluate a model's discriminative power.
6
Binary Logistic Regression: From Fit to Evaluation
Fit, interpret, and evaluate a binary logistic regression model using a real dataset.
Previous module
Inference and Model Assessment for GLMs
Next module
Multinomial and Ordinal Logistic Regression