Course Contents

Concepts of Probability Definition of probability, probability of events, Simple Sample space, Rules of probability, mutually exclusive events and addition rule, the multiplication rule (for independent events only), combining the rules of probability. The binomial probability distribution (application only), Normal probability distribution, the standard normal distribution, application of normal distribution Inference involving One population Hypothesis testing about mean, Hypothesis testing about the binomial probability of success. Inference involving Two populations Dependent and Independent samples, Hypothesis testing concerning the mean difference using two dependent samples, Hypothesis testing concerning the difference between means using two independent samples, Hypothesis testing concerning the difference between proportions using two independent samples. Checking of assumptions of normality and equality of variances for t-distribution using SPSS. Analysis of variance Introduction to the Analysis of variance technique, the logic behind ANOVA, applications of single factor ANOVA (one way & two-way analysis) Linear Correlation and Regression Analysis Inference concerning the slope of the regression line, Checking of assumptions of normality and equality of variances for regression. Elements of Non-Parametric Statistics Non-Parametric Statistics, comparing statistical tests, The Sign test, Wilcoxon Signed Rank test, The Runs test, Median test, Mecnemar test for paired observations, Wilcoxon Mann-Witney test, Kruskal Wallis test, Friedman test and Multiple Comparison with GFriedman test.