Course Content and Outline
Inferential Statistics: sampling
distributions, point and interval estimation.
Criteria of good estimators, maximum
likelihood estimators and their large sample properties.
Statistical hypotheses and tests, including
most powerful and uniformly most powerful tests and likelihood ratio tests,
classical tests of parametric hypotheses about means and variances of normal
populations, tests for proportion, chi-square tests of homogeneity, independence,
goodness-of-fit, sign test and Wilcoxon test.
Introduction to regression theory. Least
squares estimation, hypothesis testing, prediction, regression diagnostics,
residual analysis, variance stabilizing transformations, regression using
indicator variables, variable selection, and model building.
The basics of hypothesis testing using
likelihood ratios, point and interval estimation, including consistency,
efficiency, and sufficient statistics, and some nonparametric methods are
presented. Multiple regression and trend surface analysis. Analysis of
MODE OF DELIVERY
and problem assignments