By the end of the course unit, students should be able to;

Ø  Estimate parameters using different methods

Ø  Test hypotheses

Ø  State and prove the different properties of estimators.

COURSE CONTENT

Ø  Introduction [definition of concepts]

Ø  Estimation methods of estimators

·         Point estimation methods [method of moments, maximum likelihood estimation method, Bayesian method, least squares estimation method ,minimum chi-square method etc]

·         Interval estimation methods

Ø  Properties of estimators/ evaluating the goodness of estimators

·         Unbiasedness [tests for unbiasedness of estimators]

·         Sufficiency

·         Completeness

·         Best linear unbiased estimators

·         Uniform minimum variance unbiased estimators

·         Efficiency

·         Consistency

Ø  Distributions with their derivations

·         Normal distribution [mgf,mean and variance]

·         T-distribution

·         Fisher’s distribution

Ø  Hypothesis testing

·         Definition of terms and concepts

·         Best critical region

·         Neyman Pearson Lemma for the best critical region

·         Likelihood ratio tests

REFERENCES

·         Probability and mathematical statistics by Prasanna Sahoo

·         Introduction to mathematical statistics by Robert V. Hogg and Allen T. Craig