Graduate Catalog 2010-12

Statistics

 

Note: Additional work will be required for graduate credit in 400-level courses.

STAT 416       Mathematical Statistics II                                                                           3
Prereq.:  STAT 315. Continuation of theory and applications of statistical inference. Elements of sampling, point and interval estimation of population parameters, tests of hypotheses, and the study of multivariate distributions. 

STAT 425       Loss and Frequency Distributions and Credibility Theory                   3
Prereq.:  STAT 416 (may be taken concurrently). Topics chosen from credibility theory, loss distributions, simulation, and time series. Spring. 

STAT 453       Applied Statistical Inference                                                                      3
Prereq.:  Graduate standing with at least one course in statistics or STAT 315 or permission of instructor. Statistical techniques used to make inferences in experiments in social, physical, and biological sciences, and in education and psychology. Topics included are populations and samples, tests of significance concerning means, variances and proportions, and analysis of variance. No credit given to students with credit for STAT 201 or 216. Spring, Summer. 

STAT 455       Experimental Design                                                                                  3
Prereq.:  STAT 201 or 216 or 416 or permission of instructor. Introduction to experimental designs in statistics. Topics include completely randomized blocks, Latin square, and factorial experiments. Fall. (O) 

STAT 456       Fundamentals of SAS                                                                                 3
Prereq.:  CS 151 and STAT 201 or 216 or equivalent. Introduction to statistical software. Topics may include creation and manipulation of SAS data sets; and SAS implementation of the following statistical analyses:  basic descriptive statistics, hypotheses tests, multiple regression, generalized linear models, discriminant analysis, clustering and analysis, factor analysis, logistic analysis and model evaluation. This course is cross-listed with MKT 444. Spring. (E) 

STAT 465       Nonparametric Statistics                                                                            3
Prereq.:  STAT 201 or 216 or 416 or permission of instructor. General survey of nonparametric or distribution-free test procedures and estimation techniques. Topics include one-sample, paired-sample, two-sample, and k-sample problems as well as regression, correlation, and contingency tables. Comparisons with the standard parametric procedures will be made, and efficiency and applicability discussed. Fall. (E) 

STAT 476       Topics in Statistics                                                                                      3
Prereq.:  Permission of instructor. Topics depending on interest and qualifications of the students will be chosen from sampling theory, decision theory, probability theory, Bayesian statistics, hypothesis testing, time series or advanced topics in other areas. May be repeated under different topics to a maximum of 6 credits. Spring. (O) 

STAT 521       Introduction to Data Mining                                                                      4
Prereq.:  STAT 104 or STAT 200 or STAT 215 or STAT 315 or permission of department chair. Data mining models and methodologies. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, multiple regression, model building, k-means clustering, and classification and regression trees.

STAT 522       Data Mining Methods                                                                                 4
Prereq.:  STAT 521; STAT 315; STAT 201 or STAT 216 or STAT 416 or STAT 453 or permission of department chair. Data mining models and methodologies. Topics may include model evaluation techniques, hierarchical clustering methods, logistic regression, k-nearest neighbor classification, decision trees, the C4.5 algorithm, and neural networks. Spring.

STAT 523       Applied Data Mining                                                                                   4
Prereq.:  Admission to M.S. in Data Mining and STAT 416 and STAT 522 or permission of department chair. Advanced investigation of data mining models and methodologies. Topics may include dimension reduction methods, Kohonen networks clustering, association rules using the a priori and generalized rule induction algorithms, naive Bayes classification and Bayesian networks, and genetic algorithms. Fall.

STAT 525       Web Mining                                                                                                 3
Prereq.:  STAT 521 or permission of department chair. Methods and techniques for mining information from web structure, content, and usage. Topics may include web log cleaning and filtering, de-spidering, user identification, session identification, path completion exploratory data analysis for web mining, and modeling for web mining, including clustering, association, and classification. Spring.

STAT 526       Data Mining for Genomics and Proteomics                                             3
Prereq.:  STAT 521 or permission of the instructor. Topics include selection of data mining methods appropriate for the goals of a biomedical study (supervised versus unsupervised, univariate versus multivariate), analysis of gene expression microarray data, biomarker discovery, feature selection, building and validation of classification models for medical diagnosis, prognosis, and drug discovery. Fall.

STAT 527       Text Mining                                                                                                 3
Prereq.:  STAT 521 or permission of the instructor. Intensive investigation of text mining methodologies, including pattern matching with regular expressions, reformatting data, contingency tables, part-of-speech tagging, and top-down parsing. Extensive use of Perl and Perl modules to analyze text documents. Spring.

STAT 529       Current Issues in Data Mining                                                                   3
Prereq.:  Admission to the M.S. Data Mining program or permission of department chair. Topics depending on interest and qualifications of the students will be chosen from recent developments in data mining, including statistical pattern recognition, statistical natural language processing, bioinformatics, text mining, and analytical CRM. Use of statistical and data mining software. May be repeated under different topics to a maximum of 9 credits. Migration and Attrition. Extensive use of SPSS' Clementine data mining software is required. Irregular. 

STAT 551       Applied Stochastic Processes                                                                    3
Prereq.:  STAT 315 and MATH 228 or permission of instructor. An introduction to stochastic processes. Topics include Markov, Poisson, birth and death, renewal, and stationary processes. Statistical inferences of Markov processes are discussed. Fall. (O)

STAT 567       Linear Models and Time Series                                                                3
Prereq.:  STAT 416. Introduction to the methods of least squares. Topics include general linear models, least squares estimators, inference, hypothesis testing, and forecasting with ARIMA models. Spring.

STAT 570       Applied Multivariate Analysis                                                                    3
Prereq.:  MATH 228; STAT 416 or, with permission of instructor, STAT 201, 216, or 453. Introduction to analysis of multivariate data with examples from economics, education, psychology, and health care. Topics include multivariate normal distribution, Hotelling's T2, multivariate regression, analysis of variance, discriminant analysis, factor analysis and cluster analysis. Computer packages assist in the design and interpretation of multivariate data. Spring. (O) 

STAT 575       Mathematical Statistics III                                                                          3
Prereq.:  STAT 416 or equivalent. Continuation of theory and applications of statistical inference. Advanced topics in the estimation of population parameters and the testing of hypotheses. Introduction to Bayesian methods, regression, correlation and the analysis of variance. Fall. (E)

STAT 576       Advanced Topics in Statistics                                                                    3
Prereq.:  Permission of instructor. Seminar in probability theory, sampling theory, decision theory, Bayesian statistics, hypothesis testing, or other advanced area. Topic depending on needs and qualifications of students. May be repeated under different topics to a maximum of 6 credits. Irregular.

STAT 599       Thesis                                                                                                           3
Prereq.:  Permission of advisor, and a 3.00 overall GPA. Preparation of thesis under guidance of thesis advisor for students completing master's requirements under M.S. Plan A in Data Mining. On demand.

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