This course introduces the regression methods for analysing data in regression. This course emphasizes both the theoretical and the practical aspects of statistical analysis, focusing on techniques for estimating regression models of various kinds.

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).


  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




Topics and Reading.

Books with Page No.


Introduction to Regression with some basic concepts.

Basic Econometrics. (17-24)


Types of data and estimating the unknown regression parameters. Measurement Scales

Basic Econometrics.  (25-31)


Two variable Regression Analysis.

Basic Econometrics.  (37-40)

4 Population and Sample Regression Functions. Significance of Error term. Basic Econometrics.  (37-40)


Exercise and practical examples of Two variable regression Analysis

Basic Econometrics.  (52-57)


Problems of Estimation in two variable Regression model. Method of OLS.

Basic Econometrics.  (58-65)


Assumptions of CLRM and brief concept of Multicollinearity.

Basic Econometrics.  (65-75)


Standard errors of Least Square Estimates. Practices on SPSS, Minitab and R-language

Basic Econometrics. (76-79)


Gauss Markov Theorem. Coefficient of Determination. General Linear Models and its assumptions.

Basic Econometrics. (79-106)


Classical Normal Linear Regression Model. Maximum Likelihood Estimator, tests of significance for regression model

Basic Econometrics.  (107-118)

Linear Models in Matrix form. (92-95)


Two variable regression. Confidence intervals for regression parameters, Test of linearity of regression.

Basic Econometrics.  (119-146)


Normality Tests. Use of extraneous information in linear regression model. Exercise, Theorems and Appendix.

Basic Econometrics.  (147-163)


Hetroscedasticity. Tests for Hetroscedasticity. Residual analysis, Detection and study of outliers,

Basic Econometrics.   (388-408)


Autocorrelation. Polynomial regression. Simultaneous Equations.

Linear Models in Matrix form. (341-348)

Basic Econometrics.   (441-447)


Polynomial interactions, Specification of models

Linear Models in Matrix form. (349-351)


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 :
Tuseday (8:00 AM to 9:00 AM), Wensday (11:00 AM-12:00 PM), Thursday (9:00 AM-10:00 AM), Friday (10:00 AM- 11:00 AM)

Course Material