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Lower-Division Courses


Upper-Division Courses

Probability and statistics designed for students majoring in the natural sciences. Topics include descriptive statistics, probability, estimation, testing hypotheses, analysis of variance, regression and nonparametric statistics. Use of personal computers with computer software will be required. Credit will not be given for both STAT 3717 and 3743. Prereq.: Math 1549 or 1570 or 1571 or 1585H or equivalent. 4 s.h. (syllabus)

The statistical foundation of actuarial contingency models including the analysis of benefit reserves. Other topics selected from multiple life functions and decrement models, insurance models, and applications. Prereq.: STAT 3743 or consent of department chairperson. (syllabus)

Approaches to and practice with problem solving in actuarial science. Topics may include financial mathematics, financial economics, or actuarial modeling. May be repeated once. Not applicable to the mathematics major. Prereq.: STAT 4843 or consent of the instructor. 2-3 s.h.  (syllabus)

Application of regression, survey sampling, analysis of variance, design and analysis of experiments, and related topics. Prereq.: STAT 3717 or 3743 or equivalent. 3 s.h. (syllabus)

Statistical Analysis System Data and Analytics. An introduction to SAS programming for data and analytics. Topics include using SAS for data processing, manipulation, visualization, reporting, and statistical analysis. The objective is for students to develop statistical computing skills for problem solving and decision making. Prereq.: STAT 3717 or STAT 3743 or equivalent. 3 s.h. (syllabus)

A systematic introduction to data mining with emphasis on various data mining problems and their solutions. Topics include data mining processes and issues, exploratory data analysis, supervised and unsupervised learning, classification, and prediction methods. Prereq.: STAT 3717 or 3743, or consent of department chairperson. 3 s.h. (syllabus)

                                                

An introduction to the Bayesian approach to statistical inference for data analysis in a variety of applications. Data analysis using statistical software will be emphasized. Topics include: comparison of Bayesian and frequentist methods, Bayesian model specification, prior specification, basics of decision theory, Markov chain Monte Carlo, Bayes factor, empirical Bayes, Bayesian linear regression and generalized linear models, hierarchical models. Prereq.:STAT 3717 or STAT 3743 or STAT 5817 or STAT 6940 or equivalent 3 s.h. (syllabus)

Computational methods used in statistics. Topics include generation and testing of random numbers, computer intensive methods, and simulation studies. Prereq.: STAT 3717 or 3743. 3 s.h. (syllabus)

The objective of this course is to develop the skills for providing statistical consulting.  Topics include problem solving, study design, data management, application of statistical methods, and communication skills. Prereq.: STAT 5817 or equivalent. 3 s.h. (syllabus)