Course Outline

Pre-requisites: Mathematical Methods, Linear Algebra, and Introduction to Statistics.

• Random vectors and Random matrices
  Expectation vector. Variance - covariance matrices. Distribution of a linear transformation.
• Multiple Regression
  Multiple Regression model. Least squares estimators and their statistical properties. Residual analysis. Weighted least squares estimators. Test of general linear hypothesis. Multicollinearity
• Analysis of variance
  Estimation and multiple comparison. Three way Analysis of Variance. Experimental Designs: Completely Randomised Design, Randomised Block Design, Latin Square Design.
• Analysis of Covariance
  One way classification with one covariate. One way classification with two covariates. Development by the general regression significant test.
  

Prescribed Textbooks

1.

Applied Statistics. Dunn, O. and Clark, V. 1987. John Wiley.

2.

Introduction to Linear Regression Analysis. Montgomery, D. and Peck, E. 1982. John Wiley.

Recommended Textbooks

1.

Applied Linear Statistical Models. Neter, J. and Wasserman, W. 1974. Richard D. Irwin.

2.

Design and Analysis of Experiments. Montgomery, D. 1984. John Wiley.