Details of MA5212 (Spring 2021)
| Level: 5 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| MA5212 | Regression Analysis | Asok Kumar Nanda |
| Syllabus |
|---|
| Classical linear regression model, estimation and confidence interval of parameters, Gauss-Markov theorem, estimable parametric function and its BLUE, least square estimation with restrictions on parameters, testing of regression estimators, heteroscedasticity, variance stabilizing transformation, method of detecting outlier, Box-Cox method, multicollinearity and Ridge regression, autocorrelation and Durbin-Watson test, Cochrane-Orcutt method, indicator variables, non-linear regression, logistic regression. |
| Prerequisite |
|---|
| Analysis IV (MA3204) and Statistics I (MA3205) |
| References |
|---|
| Suggested Texts:
1)Brockwell, P. J. and Davis, R. A., Introduction to Time Series and Forecasting, Second Edition, Springer. 2)Drapper, N.R. and Smith,H., Applied Regression Analysis, John Wiley. 3)Gujarati,.N. and Porter, D.C., Basic Econometrics, McGraw-Hill. 4)Montgomery, D.C., Peck, E.A. and Vining, G.G., Introduction to Linear Regression Analysis, Wiley. 5)Rao, C.R., Linear Statistical Inference and Its Applications, Wiley. 6)Sengupta, D. and Jammalamadaka, S.R., Linear Models : An Integrated Approach, World Scientific. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 2 | Not Allowed |
| 2 | IP | 4 | Not Allowed |
| 3 | IP | 6 | Not Allowed |
| 4 | MR | 2 | Not Allowed |
| 5 | MR | 4 | Not Allowed |
| 6 | MS | 10 | Elective |
| 7 | MS | 4 | Not Allowed |
| 8 | MS | 6 | Not Allowed |
| 9 | MS | 8 | Not Allowed |
| 10 | RS | 1 | Not Allowed |
| 11 | RS | 2 | Elective |