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.

This is an advanced course. In this course, the students will learn about various advanced methods and their applications in many real-life problems.

After completion of the course, the student would be on an advanced level. They should be able to
- chose, motivate their choice and critically evaluate statistical analysis methods in relation
to research questions
- perform, interpret and report analysis of variance and regression analysis on data arranged to fit these analysis methods
- perform, interpret and report one of the above-mentioned analysis methods on real-life data.

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

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.