### Regression Analysis (STAT-303)

Introduction

This course introduces the regression methods for analyzing data in regression. It aims to emphasize both the theoretical and practical aspects of statistical modeling typically focusing on the techniques for estimating regression models of different kinds. It also enlightens the applications of different prediction models utilized in the both long-term and short-term time period. This coves the statistical methods related to modeling based strategies and cause and effect terminologies. Regression analysis is also used to identify the factors (independent variables) which are responsible to change the dependent variable.

Learning outcomes

• The goal is to help the students to develop a solid theoretical background in the regression analysis.
• This course helps in predicting and forecasting the results.
•  To enable the students how output depends on the other inputs(factors).

Textbooks

1. Gujrati, DN. (2004). “Basic Econometrics”, John Wiley, New York.
2. Johnston, J. and Di. Nardo, J., (1997). “Econometric Method”, 4th Edition, McGraw Hill, New York.
3. Searle, S. R. (1971), “Linear Models”, John Wiley, New York
4. Yan, X. and Zu, X. G. (2009) Linear Regression Analysis: Theory & Computing. World Scientific Publications.
5. Salvatore, D. and Reagle, D. (2002). Theory and Problems of Statistics and Econometrics, 2nd Edition. McGraw-Hills, New York.

Course Plan

Week

# Topics and Reading.

## Books

1

Introduction to Regression with some basic concepts.

Theory and Problems of Statistics and Econometrics

2

Correlation Analysis and different types of correlation

Theory and Problems of Statistics and Econometrics

3

Simple linear regression: Definition, Examples and Estimation with OLS

Theory and Problems of Statistics and Econometrics

4 Simple linear regression: Tests of significance Theory and Problems of Statistics and Econometrics

5

Simple linear regression: Test of goodness of fits, R-square and Adjusted R-square

Theory and Problems of Statistics and Econometrics

6

Simple linear regression:Properties and assumptions of the OLS

Theory and Problems of Statistics and Econometrics

7

Simple linear regression: Maximum likelihood estiomation with matrix approach

Theory and Problems of Statistics and Econometrics

8

Simple linear regression: Solution of some practicle excercises

Theory and Problems of Statistics and Econometrics

9

Multiple linear regression: Definition, estimation and applications

Theory and Problems of Statistics and Econometrics

10

Multiple linear regression: Tests of significance

Basic Econometrics

Linear Models in Matrix form

11

Multiple linear regression: Maximum likelihood estimation using matrix approach

Theory and Problems of Statistics and Econometrics

12

Multiple linear regression: Solution of practicle excercises

Theory and Problems of Statistics and Econometrics

13

Types of regression models, regression analysis with qualitative predictors estimation and interpretations

Theory and Problems of Statistics and Econometrics

14

Best subset regression: Model selection criteria's

Theory and Problems of Statistics and Econometrics

15

Residual Analysis: Outlier and influence analysis

Theory and Problems of Statistics and Econometrics

16

Work on Minitab, SPSS and R- language

The R book

Description of system of Evaluation

Exam: Mid (30%), Final (50%), Sessional (20%): Assignments, Presentations, Quizzes, Class Participation.

Lecture Time (Regular) :
Wensday (8:30 AM-10:00 AM), Thursday (11:00 AM-12:30 PM), Friday (9:00 AM-10:00 AM)

Lecture Time (Self) :
Monday (4:00 PM-5:00 PM), Wensday (3:00 PM-4:30 PM),  Thursday (2:00 PM-3:30 PM)