Course description
Introduction; Review of Matrix algebra;Practical examples of multivariate data; Preliminary data analysis; Examination of a data matrix, reduction of a data matrix; definition and calculation of sample summary statistics: means, variances, covariance’s, correlations; Examination and interpretation of sample correlation matrix; the multivariate normal distribution. Study of relationships (association); One-sample test of mean vector; simultaneous confidence intervals for detecting important components; test of structural relationship; Extension to two-sample tests; principal components and factor analysis as a means of reducing dimensionality: Calculation and interpretation of principal components and common factors.
Objectives
· To equip students with sound knowledge of extending the statistical ideas of univariate data analysis to that of multivariate;
· To equip them with skills of computing multivariate methods;
· To motivate them to apply the multivariate methods to solve real life problems.
Learning outcomes
At the end of the course students are expected to:
- State the basic statistical ideas of multivariate data analysis;
- Use the basic multivariate statistical methods and interpret them.
- Teacher: Tilahun Bedaso