STA 100 Statistical Methods (4)

Study of the methods whereby data are collected, analyzed, and presented.  Topics include: frequency distributions, measures of location, dispersion, and skewness, probability and probability distributions, and various topics in statistical inference. Class incorporates data analysis software. Meets the new General Education Mathematics requirement.

STA 225 Applied Statistical Analysis (4)

This course deals indepth with statistical methods used to analyze data.  Applications are drawn from many diverse areas.  Topics include: measures of location and scale for frequency distributions, addition and multiplication laws for probability, binomial, Poisson, and normal distributions, inferences about proportions and location parameters in onesample and twosample problems, analysis of completely randomized and randomized blocks designs, simple linear regression and correlation, sign test, median test, rank sum test, and signed rank test. Prerequisites:  MAT 112, or MAT 121 or MAT 152.

STA 290 Topics in Statistics (1-4)

An introductory course in selected topics in Statistics not currently covered in any of the listed classes. Topics are chosen to illustrate different fields and applications which are all part of Statistics.

STA 410 Applied Regression Analysis (4)

Many students in engineering and the physical sciences require a continuation and further development of statistical inference within the context of linear models. Basic statistical concepts are briefly reviewed and the ordinary least squares (OLS) approach developed. Multiple regression analysis is developed with an emphasis on model specification and assessment of model assumptions. Analysis of Covariance models are developed and studied. Matrix representations are also developed and allow a more rigorous approach. A computational environment for simulation and data analysis (for example SPSS or R) is integrated throughout the course.

STA 471 Time Series Analysis and its Applications (3)

An introduction to the theory and applications of time series analysis and modeling. The students will acquire a working knowledge of stochastic processes, time series and forecasting methods as applied in economics, finance, engineering and the natural and social sciences. Topics covered include stationary stochastic processes, AR, MA, ARMA, ARIMA, SARIMA, ARCH and GARCH processes. A computational environment for simulation and data analysis is integrated throughout the course. Prerequisite: MAT 370 with a grade of C or better.