Descriptive Statistics & Data Analysis
Code (Credit) : CUNT2484 (3-0-1)
Course Outline
Module:1
Random variables and distributions functions (univariate and multivariate); Expectations and moments.Marginal and conditional distributions. Characteristic functions.
Module:2
Standard discrete and continuous univariate distributions.Sampling distributions, Standard errors and asymptotic distributions, Distributions of order statistics and range.
Module:3
Simple non-parametric tests for one and two sample problems, Rank correlation, and test for independence.
Module:4
Analysis of variance and covariance. Fixed, random and mixed effects models. Simple and multiple linear regression, Logistic regression.
Module:5
Multivariate normal distribution, Wishart distribution, and their properties. Distributions of quadratic forms. Inference for parameters.
Module:6
Data reduction techniques: Principle component analysis, Discriminant analysis, Cluster analysis.
Module:7
Simple random sampling, stratified sampling, and systematic sampling. Probability proportional to size sampling.
Projects:
Prepare a report on Gauss Markov models
Prepare a report on correlation and regression analysis
Text Book:
- Irwin Miller and Marylees Miller, John E. Freund, Mathematical Statistics with Applications, 7th Ed., Pearson Education, Asia, 2006.
- Sheldon Ross, Introduction to Probability Models, 9th Ed., Academic Press, Indian Reprint, 2007.
- Devore, J. L.: Probability & Statistics for Engineering and the Sciences, 8th edition, Cengage Learning, 2012.
Reference Book:
- Milton, J. S. and Arnold J. C.: Introduction to Probability and Statistics: Principles andApplicationsforEngineering and theComputing Sciences, 4th edition, TataMcGraw-Hill, 2007.
- Johnson, R. A., Miller: Freund’s Probability and Statistics for Engineers, 8th edition, PHI, 2010.
- Meyer, P.L.:IntroductoryProbabilityandStatisticalApplications,2ndedition, Addison-Wesley, 1970.
- Ross, S. M.: Introduction to Probability Models, 11th edition, Academic Press, 2014.