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, estimation and simple tests of hypothesis.
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.
Probability theory and mathematical statistics at the post-calculus level. Principal topics include probability axioms, random variable, expectation, central limit theorem, special discrete and continuous distributions, and an introduction to sampling theory and data reduction.
Sampling distributions, sufficient statistics, likelihood procedures, point estimation, hypothesis testing, and confidence intervals. Other topics include Bayesian inference, multivariate transformations, and analysis of categorical data.
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.