Introduction
Regression models are ubiquitous in applied as well as methodological statistical research. In all research dimensions, we are often interested in outcomes that do not follow a normal distribution, such as binary outcomes (survived/died, successful/unsuccessful therapy) and counts (number of infections/cases of cancer/complications at a hospital or in a county). Understanding the fundamentals of these models is critical for anyone in statistical research. Regression diagnostics for outlier and influential observations are the other core objectives of this course.
Learning outcomes
Textbooks
Course Plan
Week 
Topics and Readings 
Books 
1 
Assumptions of the linear model 
Applied Regression Analysis 
2 
Regression diagnostics: Outlier detection methods 
Applied Regression Analysis 
3 
Regression diagnostics: Influential observation detection methods 
Applied Regression Analysis 
4  Multiplecase Diagnostics  Applied Regression Analysis 
5 
Regression diagnostics estimation and computation with R and Mathematica 
Applied Regression Analysis 
6 
Recent Developments, and HighBreakdown Diagnostics 
Applied Regression Analysis 
7 
SteinRule Shrinkage Estimator: Motivation for Shrinkage: SteinRule in the Regression Context. 
Introduction to Linear Regression Analysis 
8 
Properties of the SteinRule Estimator and its Extensions. 
Basic Econometrics 
9 
Robust Regression for Nonnormal Errors 
Basic Econometrics 
10 
Testing a Nonlinear Specification 
Basic Econometrics 
11 
Measures of Nonlinearity.

Applied Regression Analysis 
12 
Orthogonality 
Applied Regression Analysis 
13 
Distribution of Quadratic forms 
Applied Regression Analysis 
14 
Resampling Techniques 
Introduction to Linear Regression Analysis 
15 
Jackknifing, Bootstrapping.

An Introduction to Generalized Linear Models, Generalized Linear Models 
16 
Robust regression estimation, Resampling, bootstrapping and Jackknifing approaches with r 
An Introduction to Generalized Linear Models, Generalized Linear Models 
Description of system of Evaluation
Exam: Mid (30%), Final (50%), Sessional (20%): Assignments, Presentations, Class Participation Quizzes
Lecture TIme: Tuseday (9:00 AM to 12:00 PM)