A critical thinking course on the basic foundation of Statistics for non-majors, allowing them to better prepare, develop and harness discipline-specific skills.

Pre-req: (ACT Math with a score of 17 or SAT MATH SECTION SCORE with a score of 460 or SAT Mathematics Before Mar. 16 with a score of 400 or Placement Math SP20 with a score of 237 or MTH 102 with a minimum grade of C or MTH 102B with a minimum grade of C) and STA 150L (may be taken concurrently) with a minimum grade of D.

A critical thinking course on the basic foundation of Statistics for non-majors with algebra review, allowing them to better prepare, develop and harness discipline-specific skills.

Pre-req: STA 150L (may be taken concurrently) with a minimum grade of D.

A lab to provide training in using the SPSS software in Foundations of Statistics for non-majors.

Pre-req: STA 150 (may be taken concurrently) with a minimum grade of D or STA 150B (may be taken concurrently) with a minimum grade of D.

A critical thinking course in applied statistical reasoning covering basic probability, descriptive statistics and fundamental statistical inference procedures. Parameter estimation and hypothesis testing for variety of situations with wide applications.

Pre-req: ACT Math with a score of 21 or SAT Mathematics Before Mar. 16 with a score of 500 or SAT MATH SECTION SCORE with a score of 530 or Placement Math After SP17 with a score of 102 or MTH 121 with a minimum grade of C or MTH 121B with a minimum grade of C or MTH 122 with a minimum grade of C or MTH 127 with a minimum grade of C or MTH 130 with a minimum grade of C.

Use of statistical packages; introduction to descriptive, probability and sampling distributions; forecasting, inferences concerning one and two samples; simple and multiple regression, analysis of variance and covariance.

Pre-req: STA 225 with a minimum grade of C or MTH 229 with a minimum grade of C or STA 150 with a minimum grade of C or STA 150B with a minimum grade of C.

Statistical methods in scientific/engineering research, with emphasis on applications. Probability modeling, experimental design/survey sampling, estimation/hypothesis testing procedures, regression, ANOVA/factor analysis. Implementation using statistical software such as Excel/SAS.

Pre-req: MTH 229 with a minimum grade of C or MTH 229H with a minimum grade of C.

Determining regression models; deriving parameter estimates using calculus; detailed coverage of tests of assumptions and remedial procedures (transformations and weithted least-squares); multiple and polynomial regression; tests and corrections for autocorrelation.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Analysis of variance an covariance models with derivations using calculus; detailed testing of model assumptions and remedial measures (as transformations) to yield adequate models; use of various statistical designs.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Coverage of a variety of nonparametric or distribution-free methods for practical statistical inference problems in hypothesis testing and estimation, including rank procedures and randomization procedures.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Finding statistical models to represent various time dependent phenomena and processes; coverage of a variety of forecasting techniques, with an emphasis on adaptive, regression, and Box-Jenkins procedures.

Pre-req: STA 445 with a minimum grade of C.

Coverage of the theory and applications of a variety of sampling designs; sample size determination; ratio and regression estimates; comparisions among the designs.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Introduction to statistical learning techniques for analyzing high dimensional data. Topics include data mining strategy, explanatory analysis, predictive modeling techniques and model assessment.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Probability spaces, conditional probability, and applications. Random variables, distributions, expectation, and moments.

Pre-req: MTH 230 with a minimum grade of C or STA 345 with a minimum grade of C.

Statistical inference: estimation of parameters, tests of hypotheses. Regression, analysis of variance.

Pre-req: STA 445 with a minimum grade of C.

Introduction to the commonly used statistical computing techniques, procedures and methods, with extensive use of R language and environment, and SAS for statistical computing and graphics.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Review of probability theory. Topics include stationary processes, discrete and continuous time Markov chains, Markovian queuing systems, random walks, renewal processes, Brownian motion and Markov Chain Monte Carlo.

Pre-req: STA 445 with a minimum grade of C.

Survival and hazard functions, parametric and non-parametric methods, models and inferences for survival data, and regression diagnosis.

Pre-req: MTH 230 with a minimum grade of C and (STA 345 with a minimum grade of C or STA 445 with a minimum grade of C).

Courses on special topics in statistics not listed among the current offerings.

A faculty, supervised, indvidualized course of study of a topic in statistics.