Course descriptions for Statistics

Includes organization and classification of data, graphic representation, measures of central tendency and variability, percentiles, normal curves, standard scores, correlation and regression, introduction to statistical inference, and the use of computers for statistical calculations. (Offered fall and spring semester.)

Self-paced seminar series to learn and apply statistical computing software. Recommended as a co or pre-requisite for STAT 250, MATH 320 or STAT 320. (Credit/No Credit Available)

Provides the models and methods used in a career in technology, engineering, and natural and biological sciences. Emphasizes the use of tables, graphs, and elementary descriptive statistical applications. Introduces statistical inferences through parameter estimation and regression modeling. Introduces students to the basic skills for using computers in statistical analyses. (Offered fall and spring semesters.)

Reviews basic introductory statistics with an overview of basic probability theory. Extends the scope of statistics by teaching theory through application with a focus on the use of programming and nonparametric resampling techniques in hypothesis (permutation) tests, sampling distributions, chi-square tests for independence and goodness of fit, bootstrap, estimation theory, one-way ANOVA and simple linear regression.

Studies a particular topic in Statistics.

Student and faculty member select topic of study and number of credits. (Offered when enrollment warrants.)

Includes simple linear regression, multiple linear regression, variable selection techniques, stepwise regression, analysis of variance (one way and two way, block and other designs), multiple comparisons, random and fixed effects models, residual analysis, and computing packages. (Credit/ No Credit Available)

Theory focused probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, order statistics, multivariate distributions, concept of random sample, and derivation of many sampling distributions.

Theory focused framework for statistical inference: point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency.