University of Hyderabad Logo School of Mathematics and Statistics University of Hyderabad

Programmes → I.M.Sc. in Mathematical Sciences and M.Sc. in Statistics-OR

← Back to Courses

Statistical Methods

Course Code: ST 305; ST 407  |  Credits: 4  |  Prerequisite: Basic Mathematics, UG Statistics

Syllabus Details

  • Unit I:(Data Handling and Multivariate Techniques) Introduction to R, data handling in R, data exploration, subsetting, handling factor variables, descriptive statistics; fitting univariate and bivariate models to data; data reduction and multivariate techniques (PCA, LDA, MDS, CA).
  • Unit II:(Design, Sampling and Estimation) Design of experiments in R: the effect of design on the analysis of data, with examples from CRD, RBD and factorial designs; survey sampling in R: SRSWR, SRSWOR, stratified and cluster sampling, with examples using the sampling and survey packages; point and interval estimation, tests for continuous data and tests for discrete data.
  • Unit III:(Regression and Simulation) Basic correlation and regression, multiple regression with basic diagnostics, ANOVA; generating random numbers in R, simulation based large sample approximation, simulation from stochastic processes, bootstrapping.
  • Unit IV:(Non-parametric and Nonlinear Methods) Non-parametric tests, density estimation, permutation tests; nonlinear and non-parametric regression: common transformations, polynomial regression, Box-Cox transformations, Lowess.

Course Learning Objective

  1. Obtain and interpret graphical and diagrammatic representations of data.
  2. Obtain and interpret summary measures of univariate, bivariate and multivariate data.
  3. Discuss the importance of statistical techniques and concepts in different areas of applied statistics.
  4. Apply parametric and non-parametric statistical techniques in decision making.
  5. Simulate appropriate random observations.
  6. Carry out basic, non-linear, non-parametric and polynomial regression on a given sample of data.
  1. Tanner, Martin A. Tools for Statistical Inference. Springer-Verlag, 2011
  2. Snedecor, George W. and Cochran, William G. Statistical Methods. 7th ed. Iowa State University Press, 1982
  3. Conover, W. J. Practical Nonparametric Statistics. 3rd ed. Wiley, 2007
  4. Ripley, Brian D. Stochastic Simulation. Wiley-Interscience, 2006
  5. Kennedy, William J. and Gentle, James E. Statistical Computing. Taylor & Francis, 1980
← Back to Courses