Introduction
This course is designed for M.Phil. in Statistics students. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis. Generally, regression analysis is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).
Learning Objectives
Textbooks
Course Plan
Week |
Topics and Readings |
Books |
1 |
Introduction to linear models |
Applied Regression Analysis |
2 |
Functionally related models mean related model |
Applied Regression Analysis |
3 |
Least squares and unbiased estimation |
Applied Regression Analysis |
4 |
Best Linear U Best Linear Unbiased Estimation. |
Applied Regression Analysis |
5 |
Multiple Regression Analysis: Estimation and exercises |
Applied Regression Analysis |
6 |
Multiple Regression Estimation through matrix approach and related exercises |
Applied Regression Analysis |
7 |
Various approaches of subset selection procedures |
Introduction to Linear Regression Analysis |
8 |
Heteroscedasticity: Definition, Reasons, Consequences, Detections and remedial measures |
Basic Econometrics |
9 |
Autocorrelation: Definition, Reasons, Consequences, Detections and remedial measures |
Basic Econometrics |
10 |
Multicollinearity: Definition, Reasons, Consequences, Detections and remedial measures |
Basic Econometrics |
11 |
Ridge Regression Estimation and Properties |
Applied Regression Analysis |
12 |
Choice of biasing parameters |
Applied Regression Analysis |
13 |
Predictions from Regression |
Applied Regression Analysis |
14 |
Choosing a Regression Model |
Introduction to Linear Regression Analysis |
15 |
Generalized Linear Models: Definition, Estimations with Examples |
An Introduction to Generalized Linear Models, Generalized Linear Models |
16 |
Some Generalized Linear Models i.e. Poisson, Logistic, Gamma, Inverse Gaussian Regression and applications |
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 (2:00 PM to 5:00 PM)