3 Hours/Week, 3 Credits

Bivariate quantitative data: bivariate normal distribution, marginal distribution, conditional distribution. Correlation analysis: basic idea of correlation and coefficient of correlation, rank correlation and correlation ratio, fourfold and tetrachoric correlation, intra-class correlation, serial and bi-serial correlation, spurious correlation, non-sense correlation, poly-choric correlation. Simple linear regression: method of least squares, regression line, regression coefficients, regression curves from bivariate distributions. Multiple linear regression: three variable regression, estimation of parameters and standard error, separation of effects, multiple and partial correlation. General linear regression model: OLS estimators, Gauss-Markoff theorem, estimation of error variance, hypothesis testing. Polynomial regression: concepts of polynomial regression, estimating and testing in polynomial regression model, finding the degree of polynomial. Residual analysis: basic concepts, analysis of residuals by graphs, lack of fit of model adequacy.