Details of MA5212 (Spring 2025)
Level: 5 | Type: Theory | Credits: 4.0 |
Course Code | Course Name | Instructor(s) |
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MA5212 | Regression Analysis | Anirvan Chakraborty |
Syllabus |
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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 |
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Analysis IV (MA3204) and Statistics I (MA3205) |
References |
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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 |
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1 | IP | 2 | Not Allowed |
2 | IP | 4 | Not Allowed |
3 | IP | 6 | Not Allowed |
4 | MP | 2 | Not Allowed |
5 | MP | 4 | Not Allowed |
6 | MR | 2 | Not Allowed |
7 | MR | 4 | Not Allowed |
8 | MS | 10 | Elective |
9 | MS | 4 | Not Allowed |
10 | MS | 6 | Not Allowed |
11 | MS | 8 | Not Allowed |
12 | RS | 1 | Not Allowed |
13 | RS | 2 | Elective |