Stat 203: Generalized Linear Models

Catalog course description: This course covers simple linear regression and associated special topics, multiple linear regression, indicator variables, influence diagnostics, assumption analysis, selection of ‘best subset’, nonstandard regression models, logistic regression, and nonlinear regression models. This course, along with Statistics 131 and 202, also serves as preparation for Actuarial Exam SRM. Additionally, this course, along with Statistics 131, 202, 320 and 352, serves as preparation for Actuarial Exam MAS I.

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