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Midterm prep: categorical data, GLMs
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Module 7
Applied GLM: Logistic Regression
1
Binary Logistic Regression: Model and Assumptions
Write the binary logistic regression model using the logit link and state its key assumptions.
2
Interpreting the Intercept in Logistic Regression: Baseline Odds
Interpret the intercept of a logistic regression model in terms of baseline odds.
3
Interpreting Regression Coefficients as Log-Odds and Odds Ratios
Interpret regression coefficients as log-odds and odds ratios for dichotomous, polytomous, and continuous predictors.
4
Confidence Intervals for Regression Coefficients and Odds Ratios
Construct a confidence interval for a regression coefficient and transform it into a confidence interval for an odds ratio.
5
Significance Testing in Logistic Regression
Use Wald or Likelihood Ratio tests to assess the significance of predictors in a logistic regression model.
6
Pearson Chi-Square for Logistic Regression Goodness-of-Fit
Explain the role of the Pearson chi-square statistic in assessing the goodness-of-fit of a logistic regression model.
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Hypothesis Testing in GLMs
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Exam Strategy and Application