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• Week 1: Introduction of Tests of Hypotheses: Simple and composite hypotheses, critical regions.
• Week 2: Neyman-Pearson Lemma, Statement, proof and its examples
• Week 3: Power functions, most powerful tests and uniformly most powerful tests
• week 4: Deriving tests of Hypothesis concerning parameters of Normal distribution
• Week 5: Deriving tests of Hypothesis concerning parameters in exponential, gamma with examples
• Week 6: Deriving tests of Hypothesis concerning parameters in uniform distributions
• Week 7: Randomized Tests and Unbiased tests
• Week 8: Maximum Likelihood estimation and examples with different distributions
• Week 9: Generalized Likelihood Ratio test
• Week 10: Generalized Likelihood ratio tests for Location parameter of Normal distribution when dispersion is unknown as well as known
• Week 11: Likelihood ratio tests and their asymptotic properties
• Week 12: Wilks Theorem and its applications
• Week 13: Sequential Tests: SPRT and its properties
• Week 14: Goodness of fit of Wilks theorem and Power of chi square test by using R language and Central Limit Theorem , Statement and application by using R language
• Week 15: Interval Estimation: Pivotal and other methods of finding confidence interval
• Week 16: Shortest confidence interval, optimum confidence interval. Bayes’ Interval estimation