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Statistical methods of analyzing time series. Model identification, estimation, forecasting, and spectral analysis will be discussed. Applications in a variety of areas including economics and environmental science will be considered.
Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, sub sampling, regression estimates, and multi-frame techniques are covered.
Terminology, techniques, and management of Data Mining for biostatisticians.
Data Analysis for biostatisticians in the biomedical and pharmaceutical fields.
Computational methods using statistical packages and programming.
Development of statistical concepts and theory underlying procedures used in statistical process control applications and reliability.
Development and application of two-sample inference, analysis of variance and multiple regression. Assumptions, diagnostics and remedial measures are emphasized. Computer statistics packages are utilized.
Introductions to the fundamentals of probability theory, random variables and their distributions, expectations, transformations of random variables, moment generating functions, special discrete and continuous distributions, multivariate distributions, order statistics, and sampling distributions.
Theory of statistical estimation and hypothesis testing. Topics include point and interval estimation, properties of estimators, properties of test of hypotheses including most powerful and likelihood ratios tests, and decision theory including Bayes and minimax criteria.