Statistics
continued
Graduate Courses
The faculty has approval to offer the following courses in the academic years 20032004 and 20042005; however, not all courses are taught each semester or summer session. Students should consult the Course Schedule to determine which courses and topics will be offered during a particular semester or summer session. The Course Schedule may also reflect changes made to the course inventory after the publication of this catalog.
Unless otherwise stated below, each course meets for three lecture hours a week for one semester.
Mathematics: M
384C. Mathematical Statistics.
Same as Computational and Applied Mathematics 384R. General theory of mathematical statistics. Hypothesis testing, estimation, decision theory. Prerequisite: Graduate standing, and Mathematics 378K or consent of instructor or the graduate adviser in mathematical statistics.
384D. Mathematical Statistics.
Same as Computational and Applied Mathematics 384S. Continuation of Mathematics 384C. Prerequisite: Graduate standing, consent of instructor, and Computational and Applied Mathematics 384R or Mathematics 384C.
384E. Analysis of Variance.
Analysis of variance, including one and twoway layouts; components of variance; factorial experiments; balanced incomplete block designs; crossed and nested classifications; fixed, random, and mixed models; split plot designs. Prerequisite: Graduate standing, and Mathematics 378K or the equivalent or consent of instructor.
384F. Design of Experiments.
Design of experiments, including 2^{n} and 3^{n} factorial experiments, confounding, fractional factorials, sequential experimentation, orthogonal arrays, Doptimal experiments, and response surface methodology. Prerequisite: Graduate standing, and Mathematics 378K or the equivalent or consent of instructor.
384G. Regression Analysis.
Fitting linear models to data by the method of least squares, choosing best subsets of predictors, and related materials. Prerequisite: Graduate standing and consent of instructor.
384H. Multivariate Statistical Analysis.
Introduction to the general multivariate linear model; a selection of techniques, such as principle component, factor, and discriminant analysis. Prerequisite: Graduate standing and consent of instructor.
385C. Theory of Probability.
Same as Computational and Applied Mathematics 384K. Prerequisite: Graduate standing and consent of instructor.
385D. Theory of Probability.
Same as Computational and Applied Mathematics 384L. Continuation of Mathematics 385C. Prerequisite: Graduate standing, consent of instructor, and Computational and Applied Mathematics 384K or Mathematics 385C.
394C. Topics in Probability and Statistics.
Same as Computational and Applied Mathematics 394C. Recent topics have included nonparametric statistics, advanced probability. May be repeated for credit when the topics vary. Some topics are offered on the credit/no credit basis only; these are identified in the Course Schedule. Prerequisite: Graduate standing and consent of instructor.
384J. Frequency Data.
Analysis of data from discrete probability models. Topics include logit and probit regression models and the analysis of complex contingency tables. Prerequisite: Graduate standing, and Mathematics 378K or the equivalent or consent of instructor.
384L. Applied Statistics.
Data analysis and statistical inference. Topics include contingency tables, logistic regression, and generalized linear models. Prerequisite: Graduate standing, and Mathematics 378K or the equivalent or consent of instructor.
384P. Quality Assurance.
Shewhart and cumulative sum control charts, acceptance sampling, offline quality control; Taguchi methods. Prerequisite: Graduate standing, and Mathematics 378K or the equivalent or consent of instructor.
398R. Master's Report.
Preparation of a report to fulfill the requirement for the master's degree under the report option. The equivalent of three lecture hours a week for one semester. Offered on the credit/no credit basis only. Prerequisite: Graduate standing in statistics and consent of the supervising professor and the graduate adviser.
