DEGREE

Course Description

Measures of central tendency: Mean, Median and Mode and their implications; Measures of Dispersion: Range, Mean deviation, Standard deviation, Coefficient of Variation ( C.V.) , Skewness, Kurtosis. Time series analysis: Concept, Additive and Multiplicative models, Components of time series, Trend analysis: Least Square method - Linear and Non-Linear equations,  Applications in business decision-making. Index Numbers:- Meaning , Types of index numbers, uses of index numbers, Construction of Price, Quantity and Volume indices:- Fixed base and Chain base methods.  Correlation analysis; Regression analysis; Linear simple and multiple regressions, Linear Probability Model (LPM); Non-linear regressions; Cobb-Douglas production function, Dummy Variables; Logit model and Probit model. Concept of probability and its uses in business decision-making; Addition and multiplication theorems; Bayes’Theorem and its applications. Probability Theoretical Distributions: Concept and application of Binomial; Poisson and Normal distributions,  standard normal distribution,  Z-scores and their applications. Probability:. Estimation Theory and Hypothesis Testing: Sampling theory; Formulation of Hypotheses; Application of Z test, t-test, F-test and Chi-Square test.  Computer application in statistical data processing and analysis

Course Description

This is a Demo of how quantitative units may be assessed at the end of semester. The ideas may be polished further. For the sake of the demo, the access restrictions (Password and use of safe exam browser) have been removed.

Course Purpose

This is a module meant for any student who is struggling to understand the basic concepts in statistics. The course creates a forum for giving feedback to students on the basis of their statistical questions. The main approach will be through guided online exercises which are system managed but the facilitator will from time to time revise the exercises and sometimes engage the learner directly. Face to face sessions may be arranged when it becomes absolutely necessary.

Outline (Not all concepts are currently implemented)

1. Measures of central tendency, location and dispersion; arithmetic mean, median, mode, standard deviation, skewness, quartiles, percentiles.  
2. Frequency distributions
3. Basic probability theory; event, compound events, conditional and joint probability 
4.  Probability distributions and mathematical expectation
5. Standard probability distributions. 
6. The standard normal distribution and Z-Scores
7. Sampling distributions and applications and use of standard statistical tables
8. Hypothesis Testing; Introduction to Hypothesis; One sample, Independent and paired Z-Test and T-test 
   
9. One Way ANOVA 
10. Chi Square; Goodness of fit and Contingency table analysis 
11. Linear Regression and Correlation; Pearson's Correlation coefficient and Spearmans rank Correlation coefficient. 
12. Simple linear regression. Properties of regression, Error of estimation, Coefficient of determination, Hypothesis testing involving the slope, Interpreting computer output of multiple regression. 
13. Index Numbers; Simple and weighted index numbers, Laspeyres and Paasche indices, Index numbers in practice. 
14. Time seriesn analysis; Introduction, Structure of a time series, Components of a time series. Time series smoothing, Forecasting. 
15. Non-Paramentric tests; Sign tests, The tests of randomness, Wilcoxon signed rank tests, The Mann – Whitney U tests. The Kruskal – Wallis Test. Kolmogorov-Smirnov Test for Normality.