The course is aimed at enabling the student to understand:
Primary and Secondary data collection techniques
Statistics: Meaning, Importance, Limitations, Classifications and Tabulation of data, discrete and continuous variables. Frequency Distributions and Cumulative frequency distribution, Diagrammatic and Graphical presentation of data
Measure of Central Tendency: Mean, Median and Mode
Measure of Dispersion Range, Quartile Deviation, Mean Deviation and standard deviation, coefficient of variation.
Correlation and Regression analysis
Scaling Techniques: Purpose of Scaling Techniques, Types of scales: Nominal, Ordinal, Interval and Ratio Scales.
Parametric tests: Sampling Distribution and Standard Error. Element of Testing a Statistical Hypothesis- Formulation of the problem, Types of errors. Level of significance, large sample test for proportions, single mean and difference in two means. Small sample test- Application of Student’s t- test for small sample for single mean, difference in two means (independent and paired-t). ANOVA
Non-parametric analysis: Chi-square test for population variance, Krukal Wallis H Test, Mann Whitney U – test.
SAS, AMOS, SPSS, etc.
Basics of SPSS
Reading Data, Using Data Editor, Examining summary statistics for individual variables, creating and editing charts, working with output, sorting and selecting data.
Presentation and Report writing:
The elements of research project