Objectives: the students will conduct more advanced regression-based statistical analyses, including: simple linear regression and correlation analysis; multiple linear regression; logistic regression; Cox proportional hazards models. Issues of model diagnostics and analysis of residuals, model comparison and model building, and strategies for univariable and multivariable analyses will be discussed. The analyses will be conducted in SPSS.
Course Content
Correlation and simple linear
regression | Correlation and simple linear regression |
Inference for simple linear
regression | Model formulation and estimation in simple linear regression; examination of residuals and model assumptions; prediction and explained variation |
Multiple Linear Regression | multiple linear regression: model assumptions and interpretation; diagnostics; |
Model building | model comparison and model building in multiple linear regression; polynomial regression; ANCOVA and ANOVA as multiple linear regression |
Regression for proportions | Logistic regression for binary outcomes; model interpretation; comparing several groups |
Logistic regression | Multiple logistic regression; model building and model comparison |
Survival analysis | Kaplan-Meier curves and log-rank test |
Cox proportional hazards
model | Regression methods for survival data: Cox proportional hazards model |
2016 Syllabus