Coming soon
Regression Analysis
The students would be exposed to the concepts of correlation and regression. Emphasis will be laid on diagnostic measures such as autocorrelation, multicollinearity and heteroscedasticity. This course would prepare students to handle their data for analysis and interpretation.
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT222 |
Regression Analysis (Theory) |
The students will be able to –
CO32: Compute and interpret the results of Bivariate and Multivariate Regression and Correlation Analysis for forecasting CO33: Develop a deeper understanding of the linear regression model and its limitations CO34: Determine whether a regression model is significant. CO35: Recognize regression analysis applications for purposes of description and prediction. CO36: Recognize some potential problems if regression analysis is used incorrectly |
Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities |
Classroom Quiz Assignments Class Test Individual Presentation |
Introduction to correlation and its types, Measures of correlation coefficient, multiple and partial correlation, intra class correlation and correlation ratio. Problem of correlated errors: Autocorrelation , Durbin Watson Statistics, Removal of auto Correlation by transformation. Analysis of collinear data, Detection and correction of multicollinearity, Coefficient of determination.
Linear regression analysis, method of least square for regression curve fitting, regression coefficient and properties. Multiple and partial regression, examine the multiple regression equation, concept of weighted least square, regression equation on grouped data, various methods of selecting the best regression equation.
Linear estimation, Gauss-Markoff's theorem. Estimable functions, error and estimate space, normal equation and least square estimators, estimation of error variance, estimation with correlated observations, properties of least square estimators, generalized inverse of matrix and solution of normal equations, variance and covariance of least square estimators
Linear model: fixed, random and mixed effects models. Analysis of variance, multiple comparisons test: Tukey, Scheffe and Student-Newmann-Kuel,Duncan.
Regression diagnostic, normal probability plot, Goldfeld-Quandt test, Park test, Breusch- godfrey, Logistic regression
BOOKS RECOMMENDED
- Arnold, B.C., Balakrishnan, N. & Nagaraja, H.N. (1992): A First Course in Order Statistics. John Wiley.
- David, H.A. & Nagaraja, H.N. (2003): Order Statistics. 3rd Ed. John Wiley.
- Goon, Gupta & Das Gupta. (2003): Outline of Statistical Theory. Vol. I, World Press, 14th edition.
- Hogg, R.V. and Craig, A.T. (2009): Introduction to Mathematical Statistics, McMillan.
- Johnson, S. and Kotz. (1972): Distribution in Statistics, Vol.I, II. And III, Houghton and Muffin.
- Kendall, M.G. and Stuart. (1996): An Advanced Theory of Statistics, Vol. I,II. Charls Griffin.
- Mood,A.M., Graybill, F.A. and Boes, D.C.(1974): Introduction to the Theory of Statistics, McGraw Hill, third edition.
- Mukhopadhyay, P. (1996): Mathematical Statistics, New Central Book Agency (P) Ltd.
- Draper, N.R. & Smith, H. (1998): Applied Regression Analysis, 3rd Ed. JohnWiley.
- Ezekiel, M. (1963): Methods of Correlation and Regression Analysis, JohnWiley.
- Kutner, M.H., Nachtsheim, C.J. & Neter, J. (2004): Applied Linear Regression Models, 4th Ed. With Student, CD. McGraw Hill.
- Rohatgi, V.K. (2009): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern.
- C.R. Rao.(2001): Linear Models & Generalization.
- Kailath (2016): Linear Estimation.
