Applied Regression Analysis

Paper Code: 
STT 144(A)
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

The students would be exposed to the concepts of regression modelling. Emphasis will be laid on applying model and regression theory for data analysis. 

12.00
UNIT I

Residuals and their analysis, Influential observations, Power transformation for dependent and independent variables.

 

12.00
UNIT II

Robust and L-1 regression, estimation of prediction error by cross-validation and boot-strap, non-linear regression models.

 

12.00
UNIT III

Different methods of estimation (Least squares, Maximum Likelihood), Asymptotic properties of estimators. Generalized linear models.

 

12.00
UNIT IV

Analysis of binary and grouped data by using logistic models. Log-linear models. Random and mixed effect models.

 

12.00
UNIT V

Maximum likelihood, MINQUE and restricted maximum likelihood estimators of variance components, Best linear unbiased predictors (BLUP), Growth curves.

 

Essential Readings: 

Books Recommended/References:

 

  • Bates, D.M. and Watts, D.G.(1988). Nonlinear Regression Analysis and its application, Wiley, New York.
  • Cook, R.D. and Weisberg, S(1992). Residuals and inference in Regression , Chapman and Hall, London.
  • Draper, N. R. and Smith, H.(1988). Applied Regression Analysis, 3rd ed., Wiley New yark.
  • Efron,B.and Tibsirani,J.R.(1993). An Introduction to the Bootstrap, Chapman and Hal, New York.
  • Kshirsagar, A.M. (1995). Growth Curves, Marcel and Dekker, New York.
  • MoCullagh, P. and Nelder, J.A. (1989), Generalized Linear Models, 2 nd Chapman and Hall, London. . 7. Searle, S.R. (1987). Linear Models for Unbalanced data, Wiley, New York
  • Seber, G.A. and Wild, G.J.(1989)Nonlinear regression. Wiley, New York.
  • Rao, C.R. (1973): Linear Statistical Inference and its Applications, Wiley Fastern
Academic Year: 
2022-2023
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