Philosophical, ethical, and sociological issues related to statistical uncertainty and randomness.
Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance. Credit may not be obtained after receiving credit in STA 2381 or 3381.
Parametric statistical methods. Topics range from descriptive statistics through regression and one-way analysis of variance. Applications are typically from biology and medicine. Computer data analysis is required.
Introduction to the fundamentals of probability, random variables, discrete and continuous probability distributions, expectations, sampling distributions, topics of statistical inference such as confidence intervals, tests of hypotheses, and regression.
A development of regression techniques including simple linear regression, multiple regression, logistic regression and Poisson regression with emphasis on model assumptions, parameter estimation, variable selection and diagnostics.
Concepts in SAS programming including methods to establish and transform SAS data sets, perform statistical analyses, and create general customized reports. Methods from both BASE SAS and SAS SQL will be considered.
An introduction to Bayesian inference emphasizing prior and posterior distributions, estimation, prediction, hierarchical Bayesian analysis, and applications with computer implemented data analysis.
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.
Applications of probability theory to the study of phenomena in such fields as engineering, management science, social and physical sciences, and operations research. Topics include Markov chains, branching processes, Poisson processes, exponential models, and continuous-time Markov chains with applications to queuing systems. Other topics introduced are renewal theory and estimation procedures.
Statistical concepts applied to written and oral reports for consulting. For students majoring in statistics.
Topics in probability and/or statistics not covered in other courses. May be repeated for a maximum of 6 hours if the content is different.