Course Contents

WHAT IS STATISTICS Definition of Statistics, Population & sample, Descriptive and Inferential Statistics, Observations, Discrete and continuous variables, collection of primary and secondary data and sources, Introduction of SPSS. PRESENTATION OF DATA Introduction, Basic principles of classification and tabulation, Construction of a frequency distribution, Relative and Cumulative frequency distribution, Diagrams and Graphs and their construction, Bar Charts, Pie Charts, Histogram, Frequency Polygon and Frequency Curve, Historigram, Types of frequency Curves. Exercise on SPSS. MEASURES OF CENTRAL TENDENCY Introduction, Different types of Averages, Mean, Median and Mode, Quantiles, Empirical relation between Mean, Median and Mode, Merit and Demerits of various Averages, Box and Whisker Plot, Definition of outliers and their detection. Exercise on SPSS. MEASURES OF DISPERSION Introduction, Absolute and relative measures, Range, The Semi Inter-Quartile Range, Variance and Standard Deviation, Interpretation of Standard Deviation, Co-efficient of Variation, Standardized Variables. Exercise on SPSS. PROBABILITY AND PROBABILITY DISTRIBUTION Concept of discrete and continuous distribution, Normal distribution. Exercise on SPSS. SAMPLING AND SAMPLING DISTRIBUTION Introduction, sample design and sample frame, bias, sampling and non sampling errors, sampling with and without replacement, probability and non-probability sampling, Simple Random sampling, Stratified Random Sampling, Systematic and cluster sampling, Sampling Distribution for single mean and proportion. Exercise on SPSS. HYPOTHESIS TESTING Introduction, Statistical problem, null and alternative hypothesis, Type-I and Type-II Errors, Level of significance, Test Statistics, Acceptance and rejection regions, general procedure for testing of Hypothesis. TESTING OF HYPOTHESIS –INDEPENDENCE OF ATTRIBUTES Introduction, Contingency Tables, Testing of Hypothesis about the independence of attributes. Exercise on SPSS. REGRESSION AND CORRELATION Introduction, cause and effect relationships and examples, simple linear regression, estimation of parameters and their interpretation, r and R^2. Correlation, coefficient of linear correlation, its estimation and interpretation, Multiple regression and interpretation of its parameters. Exercise on SPSS.

Course Synopsis

Introduction to basic tools of statistics and applied data analysis.

Course Learning Outcomes

The course is designed to impart the knowledge of Statistics and utilizing SPSS software to present and analyze the data of different fields.