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
Textbooks
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)