To provide the students with conceptual understanding of modern statistical tools, starting from descriptive statistical measures, alternative charts and diagrams, etc. to report qualitative and quantitative data, we would like to discuss various tools and methods of modern inferential statistics to draw inference about a large population on the basis of the counterpart characteristics of a sample alone
On successful completion of the course, a student would be able to represent information in concise manner, through uses of various tables, charts, diagrams and descriptive statistical properties of it. The would also be able to draw effective prediction about unknown characteristic(s) of a population, scientifically analyzing a sample drawn. 1. Collection of Data: Classification and Tabulation: Meaning of Statistics—Variables vs. Attributes—Primary vs. Secondary Data—Population vs. Sample—Some other useful terms and concepts.
2. Charts and Diagrams: Objects of Diagrammatic Representations—Different types of Charts and Diagrams
3. Useful Mathematical Devices and Frequency Distributions: Logarithmic transformation—AP and GP series—Different types of Polynomial operations—Permutation and Combination—Summation notation—Interpolation
4. Measures of Central Tendency: Mean, Median, Mode, Quartiles, Deciles, Percentiles etc.—Concepts, Formulae and their Calculations.
5. Measures of Dispersions: Alternative absolute and relative measures of dispersions—Lorenz curve—Gini
6. Moments, Skewness and Kurtosis
7. Some discussion on Correlation and Regression
8. Index Numbers
9. Theory of Probability
10. Random Variable and Theoretical Distributions: Binomial, Poisson and Normal
11. Sampling Theory
12. Estimation and Test of Significance/Hypothesis Testing
13. Some Discussion on Time Series (If Time Permits)