Course

M484 - Regression Analysis

Course No: 
M484
Credit: 
4
Prerequisites: 
M206, M305
Approval: 
UG-Elective
Syllabus: 

Introduction to simple linear regression, least square estimation and hypothesis testing of model parameters, prediction, interval estimation in simple linear regression, Coefficient of determination, estimation by maximum likelihood, multiple linear regression, matrix representation of the regression model, estimation and testing of model parameters and prediction, model adequacy checking-residual analysis, PRESS statistics, outlier detection, lack of fit test, serial correlation and Durbin-Watson test, transformation and weighting to correct model inadequacies-variance-stabilizing transformation, generalized and weighted least squares, diagnostics for influential observations, Cook’s D test, multicollinearity-sources and effects, diagnosis and treatment for multicollinearity, ridge regression and LASSO, bootstrap estimation, dummy variable model, variable selection and model building–stepwise methods, polynomial regression and interaction regression models, nonlinear regression, generalized linear models-logistic regression and Poisson regression. 

Reference Books: 
  1. Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining, “Introduction to Linear Regression Analysis”, 5th Edition, Wiley, 2012. 

  2. N. R. Draper and H. Smith (1998), Applied Regression Analysis, 3rd Edition, New York: Wiley. 

  3. Michael H. Kutner, Chris J. Nachtsheim, and John Neter, “Applied Linear Statistical Models”, McGraw-Hill/Irwin; 5th edition, 2004. 

  4. Seber, G. A. F. and Lee, A. J., “Linear Regression Analysis”, John Wiley and Sons, 2nd Edition, 2003.
  5. N. H. Bingham, John M. Fry, “Regression: Linear Models in Statistics”, Springer Undergraduate Mathematics Series, 2010. 


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School of Mathematical Sciences

NISERPO- Bhimpur-PadanpurVia- Jatni, District- Khurda, Odisha, India, PIN- 752050

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