This course explains the concepts of random sampling, statistical inference and sampling distribution, and state and use basic sampling distributions; describe the main methods of estimation and the main properties of estimators, and apply them. Methods include matching moments, percentile matching, and maximum likelihood, and properties include bias, variance, mean squared error, consistency, efficiency, and UMVUE. This course alse construct confidence intervals for unknown parameters, including the mean, differences of two means, variances, and proportions and do test hypotheses. Concepts to be covered include Neyman-Pearson lemma, significance and power, likelihood ratio test, and information criteria. Tests should include for mean, variance, contingency tables, and goodness-of-fit.