MATH 220: Elementary Statistics. 3 CR.
Descriptive statistics, frequency distributions, sampling, estimation and testing of hypotheses, regression, correlation and an introduction to statistical analysis using computers. Prerequisite: MATH 155, MATH 156 or sufficient score on the Mathematics Placement Exam. Not open to majors in mathematics.
MATH 280: SAS Programming and Data Management. 3 CR.
Use of statistical software to manage, process and analyze data. Writing of statistical programs to perform simulation experiments. Prerequisite: MATH 220 or MATH 318 or equivalent.
MATH 285: Data Analysis. 4 CR.
Topics include experimental and survey design, distributions, variation, chance, sampling variation, computer simulation, bootstrapping, estimation and hypothesis testing using real data generated from classroom experiments and large databases. Prerequisite: MATH 206, MATH 236 or permission of instructor. Not open to students who have already earned credit in MATH 220 or MATH 318.
MATH 307: Principles of Probability and Statistics. 3 CR.
Descriptive statistics, measures of central tendency and dispersion, correlation, probability, probability distributions and statistical inference. Prerequisite: MATH 207.
MATH 318: Introduction to Probability and Statistics. 4 CR.
Descriptive statistics, counting, probability axioms, discrete and continuous random variables, expected values of random variables and sums of independent random variables, sampling distributions and the Central Limit Theorem, single and two-sample inference for proportions and means, chi-square test of independence, simple linear regression, and correlation. Prerequisite: MATH 236.
MATH 321: Analysis of Variance and Experimental Design. 3 CR.
Introduction to basic concepts in statistics with applications of statistical techniques including estimation, test of hypothesis, analysis of variance and topics in experimental design. Prerequisite: MATH 220, MATH 318 or equivalent.
MATH 322: Applied Linear Regression. 3 CR.
Introduction to basic concepts and methods in regression analysis and the application of these models to real-life situations. Prerequisite: MATH 220, MATH 318 or equivalent.
MATH 324: Applied Nonparametric Statistics. 3 CR.
Methods of analyzing data from non-normal populations including binomial tests, contingency tables, use of ranks, Kolmogorov-Smirnov type statistics and selected topics. Prerequisite: MATH 220, MATH 318 or equivalent.
MATH 325: Survey Sampling Methods. 3 CR.
Theory and practice of sampling including stratified random samples, discussion of simple random samples, cluster sampling, estimating sample size, ratio estimates, subsampling, two-state sampling and analysis of sampling error. Prerequisite: MATH 220 or MATH 318.
MATH 326: Statistical Quality Control. 3 CR.
Uses and concepts of probability and sampling procedures. Acceptance sampling by attributes and variables, Shewhart concepts of process control, control chart process capability studies, reliability and life testing. Design of sampling plans. Prerequisite: MATH 318.
MATH 327: Categorical Data Analysis. 3 CR.
Exact inference for population proportions, comparison of population proportions for independent and dependent samples, two and three-way contingency tables, Chi-square tests of independence and homogeneity, Chi-square goodness-of-fit tests and Poisson and logistic regression. Prerequisite: MATH 220 or MATH 318 or equivalent.
MATH 328: Time Series Analysis. 3 CR.
Regression and exponential smoothing methods for forecasting nonseasonal and seasonal time series, stochastic processes, Box-Jenkins' autoregressive and moving average models. Prerequisites: MATH 238 and MATH 318.
MATH 354/BIO 454: Introduction to Biometrics. 4 CR.
This course discusses the role of statistics in biological research and interpretation of biological phenomena. The course will cover topics of sampling, correlation, regression analysis, tests of hypotheses, commonly observed distributions in natural populations, nonparametric tests, goodness-of-fit tests and ANOVA. In order to fully comprehend the statistical analysis of those publications, students will review approximately half a dozen publications from different fields of biology. Prerequisites: MATH 220 or MATH 318 or equivalent.
MATH 421: Applied Multivariate Statistical Analysis. 3 CR.
Multivariate statistical methods with applications. Topics include canonical correlation, clustering, discriminant analysis, factor analysis, multivariate analysis of variance, multiple regression, multidimensional scaling and principal component analysis. Prerequisites: MATH 300 or MATH 238; and MATH 321 or MATH 322.
MATH 423: Stochastic Processes. 3 CR.
Sequences and classes of random variables. Applications to physical, biological, social and management sciences. Topics include Markov chains, branching processes, the Poisson process, queuing systems and renewal processes. Prerequisites: MATH 238 or MATH 300 or equivalent and MATH 318.
MATH 424: Statistical Decision Theory. 3 CR.
Development and use of probability and statistics for strategic decision making with applications. Topics include decision flow diagrams, analysis of risk and risk aversion, utility theory, Bayesian statistical methods, the economics of sampling, sensitivity analysis and collective decision making. Prerequisite: MATH 318.
MATH 426: Probability and Mathematical Statistics I. 3 CR.
Derivations and proofs of probability theorems, discrete and continuous univariate and multivariate random variables, conditional distributions, mathematical expectations, functions of random variables, moment generating functions, properties and derivation of estimators including the method of moments and maximum likelihood estimation. Prerequisite: MATH 318.
MATH 427: Probability and Mathematical Statistics II. 3 CR.
Limiting distributions, sampling theory and distributions, theory and applications of estimation and hypothesis testing. Prerequisite: MATH 426.
MATH 429: Research Project in Statistics. 1-3 CR.
Experience in the design, data collection and analysis for a survey or experiment. Prerequisite: Consent of the instructor.
MATH 485: Selected Topics. 1-4 CR.
Topics in advanced mathematics or statistics which are not covered in the regularly offered courses. Offered only with approval of the department head; may be repeated for credit when course content changes. Prerequisite: Consent of the instructor.
Topics have included Statistical Methods in Clinical Trials, Bayesian Statistics, Statistical Methods in Clinical Trials, Statistical Consulting, and Linear Statistical Models.
MATH 497-498: Independent Study. 1-3 CR.
Independent study in mathematics under faculty supervision. Offered only with consent of the department head. Repeatable up to 6 credit hours.