# 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.