Lecture 1 / Jan 7: Course Introduction

Lecture 2 / Jan 9: Simple Regression

Lecture 3 / Jan 14: Software & Datasets (Intro to SPSS and S Plus)

Lecture 4 / Jan 16: Multiple Regression & Graphical Methods

Jan 21: Holiday

Lecture 5 / Jan 23: Diagnostics I: Cases

Lecture 6 / Jan 28: Diagnostics II: Variances 

Lecture 7 / Jan 30: Diagnostics III: Normality

Lecture 8 / Feb 4: Variable Transformation

Lecture 9 / Feb 6: Discrete Variables

Lecture 10 / Feb 11: Collinearity

Lecture 11 / Feb 13: Variable Selection

Feb 18: Holiday

Lecture 12 / Feb 20: Generalized Linear Regression: logistic regression

Lecture 13 / Feb 25: Generalized Linear Regression: GLIM

Lecture 14 / Feb 27: Diagnostics for Logistic Regression

Lecture 15 / March 4: Missing Values

March 6: Midterm Examination

March 11 - 16: Spring Recess

Lecture 16 / March 18: Model Assessment & Validation I

Lecture 17 / March 20: Model Assessment & Validation II

Lecture 18 / March 25: Robust Regression 

Lecture 19 / March 27: Unbiased Estimation   

Lecture 20 / April 1:  Regression Tree Methods

Lecture 21 / April 3: Bootstrap & Other Simulation Methods

Lecture 22 / April 8: Modeling Strategies Discussion & Case Study by Cyrus

Lecture 23 / April 10: Prediction / Interpretation & Case Study by Shashi

Lecture 24 / April 15: Methods Comparison & Case Study by Jenny

Lecture 25 / April 17: Model Validation & Case Study by Xia

Lecture 26 / April 22: Modeling Strategies & the 7-steps Approach

Lecture 27 / April 24: Research Presentation & Case Study by Clara