Stat 203: Generalized Linear Models

Syllabus

Download the syllabus.

Slides

• Stat 203 Lecture 1: Course Intro and Exploring Data
• Stat 203 Lecture 2: Plotting Data/Two Components of a Statistical Model
• Stat 203 Lecture 3: Regression Models
• Stat 203 Lecture 4: Intro to Linear Regression
• Stat 203 Lecture 5: Simple Linear Regression
• Stat 203 Lecture 6: Multiple Linear Regression
• Stat 203 Worksheet 7: Fitting Linear Regression Using R
• Stat 203 Worksheet 8: Comparing Linear Regression Models
• Stat 203 Worksheet 9: Diagnostics, Residuals, and Leverages
• Stat 203 Worksheet 10: Exploring Assumptions
• Stat 203 Worksheet 11: Outliers and Influential Observations
• Stat 203 Lecture 12: Transforming the Response
• Stat 203 Lecture 13: Transforming Covariates
• Stat 203 Lecture 14: Polynomial Trends, Regression Splines, and Fixing Problems
• Stat 203 Lecture 16: MLE for One Parameter
• Stat 203 Lecture 17: MLE for More than One Parameter / Properties of MLEs
• Stat 203 Lecture 18: Hypothesis Testing for One Parameter
• Stat 203 Lecture 19: Two Components of a GLM
• Stat 203 Lecture 20: Structural Properties of EDMs
• Stat 203 Lecture 21: EDMs in Dispersion Model Form and Unit Deviance
• Stat 203 Lecture 22: GLMs in General and Total Deviance
• Stat 203 Lecture 23: Explorations I
• Stat 203 Lecture 24: Models for Proportions
• Stat 203 Lecture 25: Odds, Odds Ratios, and the Logit Link
• Stat 203 Exploration 26: Exploring a Health Expenditures Dataset
• Stat 203 Lecture 27: Overdispersion and Goodness-of-Fit
• Stat 203 Lecture 28: Case Study
• Stat 203 Lecture 29: Assumptions for GLMs / Residuals