Course Details
Subject {L-T-P / C} : MA6629 : Advanced Statistical Methods { 3-1-0 / 4}
Subject Nature : Theory
Coordinator : Prof. Suchandan Kayal
Syllabus
Review of Probability and Distributions. Point estimation – unbiasedness, consistency, UMVUE, sufficiency and completeness, method of moments, maximum likelihood estimation, and method of
scoring. Bayes, minimax, and admissible estimators. Interval estimation - confidence intervals
for means, variances, and proportions. Testing of Hypotheses - tests for parameters of normal
populations and for proportions, the goodness of fit test, and its applications. Analysis of Variance: One-way analysis of variance, Two-factor analysis of variance. Regression analysis: least-squares estimators, inference about regression parameters, analysis of residuals.
Course Objectives
- This is an advanced course. In this course, the students will learn about various advanced methods and their applications in many real-life problems.
Course Outcomes
After completion of the course, the student would be on an advanced level. They should be able to <br />- chose, motivate their choice and critically evaluate statistical analysis methods in relation <br />to research questions <br />- perform, interpret and report analysis of variance and regression analysis on data arranged to fit these analysis methods <br />- perform, interpret and report one of the above-mentioned analysis methods on real-life data.
Essential Reading
- V. K. Rohatgi and A.K.Md.E. Saleh, An Introduction to Probability and Statistics, Wiley
- S M Ross, Introduction to Probability and Statistics for Engineers and Scientists, Academic Press
Supplementary Reading
- J.S. Milton & J.C. Arnold, Introduction to Probability and Statistics, Mc Graw Hill
- R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye, Probability and Statistics for Engineers and Scientists, Pearson Education Inc.