STAT 100: Introduction to 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.) (Prereq: MATH 99 or ELMT Score of 50)

Units: 4

STAT 210: Statistical Computing Seminar

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)

Units: 1

STAT 250: Applied Stat:Sci Tech

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.) (Prereq: MATH 150)

Units: 4

STAT 320: Mathematical Statistics

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. (Prereq: STAT 100 or STAT 250 or MATH 320)

Units: 4

STAT 325: Experimental Design and Analysis

Studies the design, analysis, and follow-up procedures of experiments across disciplines. Includes one-way and two-way analysis of variance, completely randomized designs, factorial designs, Latin Squares, nested and split-plot design, repeated measures, block designs, analysis of covariance, multiple comparison procedures, and incomplete designs. (Prereq: MATH 320 or STAT 250 or STAT 100)

Units: 4

STAT 330: Sampling Design and Analysis

Studies the design, analysis, and follow-up procedures of sampling finite populations. Includes survey design, random, stratified, cluster, systematic sampling designs, analysis of quantitative and qualitative data collected through surveys and sampling. Emphasis on statistical considerations of sampling and non-sampling error. (Prereq: MATH 320 or STAT 250 or STAT 100)

Units: 4

STAT 395: Special Topics

Studies a particular topic in Statistics.

Units: 1 to 6

STAT 397: Independent Study

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

Units: 1 to 6

STAT 410: Applied Statistics Methods: Linear Models

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) (Prereq: STAT 250 or STAT 320 or MATH 320 or Instructor Consent)

Units: 4

STAT 420: Statistical Theory I

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. (Prereq: MATH 320 and MATH 322)

Units: 4

STAT 421: Statistical Theory II

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. (Prereq: STAT 420)

Units: 4

STAT 440: Bayesian Inference

Studies the Bayesian approach to data analysis. Includes Bayes theorem, basic concept of Bayesian statistics, prior and posterior distributions, conjugacy, credible intervals, generalized linear models, statistical inference (with comparison to frequentist approach), prior elicitation, computational methods and applications to real world problems. [(Prereq: STAT 410 and STAT 420) or (Instructor Consent)]

Units: 4