Statistics (STA)
Determining regression models; deriving parameter estimates using calculus; detailed coverage of tests of assumptions and remedial procedures (transformations and weighted least-squares); multiple and polynomial regression; tests and corrections for autocorrelation.
Pre-req: STA 545 with a minimum grade of C.
Principles of experimentation; Analysis of variance; Latin square and related designs; Factorial designs, Response Surface; Robustness; Nested and Split-Plot designs.
Pre-req: STA 545 with a minimum grade of C.
Statistical skills for biological/biomedical research, with emphasis on applications. Experimental design/survey sampling, estimation/hypothesis testing procedures, regression, ANOVA, multiple comparisons. Implementation using statistical software such as SAS, BMDP. May not be used for any degree offered by the Department of Mathematics.
Coverage of a variety of nonparametric or distribution-free markets for practical statistical inference problems in hypothesis testing and estimation, including rank procedures and randomization procedures.
Pre-req: STA 545 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; comparisons among the designs.
Introduction to statistical learning techniques for analyzing high dimensional data. Topics include data mining strategy, explanatory analysis, predictive modeling techniques and model assessment.
Probability spaces, conditional probability, and applications. Random variables, distributions, expectations, and moments.
Probability spaces, conditional probability, and applications. Random variables, distributions, expectations, and moments.
Pre-req: STA 545 with a minimum grade of C.
Survival and hazard functions, parametric and non-parametric methods, models and inferences for survival data, proportional hazard, and regression diagnosis.
Pre-req: STA 545 with a minimum grade of C.
Courses on special topics in statistics not listed among current course offerings.
A faculty supervised, individualized course of study of a topic in statistics.
Aspects of statistical modeling including model building, adequacy assessment, inference, and prediction. Applications to social biological, and medical sciences; engineering; and industry.
Theory and applications of Markov chains. (PR: MTH 545)
Pre-req: STA 545 with a minimum grade of C.
Topics in mathematical statistics including distribution theory for functions of random variables, convergence concepts, sufficient statistics, finding optimal estimates for parameters, optimal test of hypotheses. (PR: MTH 546)
Pre-req: STA 546 with a minimum grade of C.
Introduction to multivariate statistical analyses and methodologies of various types of datasets that are commonly encountered in medical, business, engineering, science, and any other data intensive disciplines.
Pre-req: STA 546 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 545 with a minimum grade of C.
An introduction to Bayesian Statistics with focus on Bayesian Modeling, inference and Data Analysis. Applications will be studies with appropriate statistical software.
Pre-req: STA 545 with a minimum grade of D.
An overview of concepts and techniques in advanced statistical learning. Topics include supervised/unsupervised learning, kernel smoothing methods, trees, random forests, association rules, neural networks and support vector machines.
Pre-req: STA 535 with a minimum grade of D.
A faculty supervised, individualized course of study of a topic in statistics.
Investigate a theoretical or applied statistics problem under faculty mentorship.
Courses on special topics in statistics not listed among the current course offerings.