Course Contents

1. Z Scores 1.1 Z scores & Normal Distribution 1.2 Z test 2. Hypothesis testing 2.1. Inferential statistics 2.2. Hypothesis formulation (Null and Alternative Hypotheses) 2.3. Level of significance acceptance and rejection regions 2.4. One tailed & two tailed hypotheses tests 2.5. Type – I & type II errors 3. T test 3.1. Testing the hypotheses; difference between mean and within means of both large & small samples 3.2. Single Sample t test 3.3. Two Independent Sample t test 3.4. Related sample t test 4. Correlation 4.1. Correlation and Hypothesis testing 4.2. Point Bi-Serial Correlation 4.3. Phi Co-efficient 5. Analysis of variance (ANOVA) 6.1 One – way ANOVA Independent Measures 6.2 One – way ANOVA Repeated Measures 6.3 Mixed method ANOVA 6. Introduction to Non-parametric statistics 6.1. Introduction to non-parametric tests 6.2. Difference in Parametric & Non Parametric Statistics 6.3. Chi- square test 7. SPSS 7.1. Data View and Variable View; Data entry 7.2. Graphical representation on SPSS 7.3. Transform Options 7.4. Composite Scores 7.5. Recoding into same & different variable 7.6. Measures of Central Tendency on SPSS 7.7. Measures of dispersion on SPSS

Course Synopsis

The course will provide an understanding of basic statistical and mathematical tools and ability to find out appropriate analysis from real life data. This course covers advanced statistical methods, inferential statistics, hypothesis testing, parametric and non-parametric statistics, linear regression analysis, and functional analysis.

Course Learning Outcomes

Understanding and mastery over inferential statistics. Hand in experience to compute statistical tests on SPSS. Read and interpret SPSS output. Students will be able to link research and statistics.