Coming soon
Regression Analysis
The students would 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.
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.
Linear regression analysis, method of least square for regression curve fitting, regression coefficient and properties.Multiple and partial regression, examing 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 erroe 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: random and fixed effects models. Analysis of variance, multiple comparisions test: Tukey, scheffe and student -Newmann-Kuel -Duncan.
Regression diagnostic, normal probability plot, Goldfeld-Quandt test, Park test, Breusch- godfrey, Logistic regression.
Books Recommended/Reference Books
1. Arnold, B.C., Balakrishnan, N. & Nagaraja, H.N. (1992): A First Course in Order Statistics. John Wiley.
2. David, H.A. & Nagaraja, H.N. (2003): Order Statistics. 3rd Ed. John Wiley.
3.Goon, Gupta & Das Gupta. (1991): Outline of Statistical Theory. Vol. I, World Press.
4. Hogg, R.V. and Craig, A.T.(1971): Introduction to Mathematical Statistics, McMillan.
5. Johnson, S. and Kotz. (1972): Distribution in Statistics, Vol.I, II. And III, Houghton and Muffin.
6. Kendall, M.G.and Stuart. (1996): An Advanced Theory of Statistics, Vol. I,II. Charls Griffin.
7. Mood,A.M., Graybill, F.A. and Boes, D.C.(1974): Introduction to the Theory of Statistics, McGraw Hill.
8. Mukhopadhyay, P. (1996): Mathematical Statistics, New Central Book Agency (P) Ltd.
9. Draper, N.R. & Smith, H. (1998): Applied Regression Analysis, 3rd Ed. JohnWiley.
10. Ezekiel, M. (1963): Methods of Correlation and Regression Analysis, JohnWiley.
11. Kutner, M.H., Nachtsheim, C.J. & Neter, J. (2004): Applied Linear Regression Models, 4th Ed. With Student, CD. McGraw Hill.
12. Rohatgi, V.K. (1984): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern.
