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

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)