Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. |

STAT 104 - Elementary Statistics |

Elementary Statistics
Skill Area II
Prereq.: MATH 101 (C- or higher) or placement exam.
Intuitive treatment of some fundamental concepts
involved in collecting, presenting, and analyzing data.
Topics include frequency distributions, graphical
presentations, measures of relative position, measures
of variability, probability, probability distributions
(binomial and normal), sampling theory, regression, and
correlation. No credit given to students with credit for
STAT 108, 200, 215, 314 or 315.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: SK2- Mathematics Requirement |

STAT 200 - Business Statistics I |

Business Statistics I
Skill Area II
Prereq.: MATH 101 (C- or higher) or placement exam.
Application of statistical methods used for a description
of analysis of business problems. The development of
analytic skills is enhanced by use of one of the widely
available statistical packages and a graphing calculator.
Topics include frequency distributions, graphical
presentations, measures of relative position, measures
of central tendency and variability, probability
distributions including binomial and normal, confidence
intervals, and hypothesis testing. No credit given to
students with credit for STAT 104, 108, 215, 314, or 315.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: SK2- Mathematics Requirement |

STAT 201 - Business Statistics II |

Business Statistics II
Skill area II
Prereq.: STAT 200 or equivalent (C- or higher).
Application of statistical methods used for a description
and analysis of business problems. The development of
analytical skills is enhanced by use of one of the widely
available statistical packages. Topics include continuation
of hypothesis testing, multiple regression and correlation
analysis, residual analysis, variable selection techniques,
analysis of variance and design of experiments, goodness
of fit, and tests of independence. No credit given to
students with credit for STAT 216, 416 or 453.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction All Sections for this Course Mathematics Department Course Attributes: SK2- Mathematics Requirement |

STAT 215 - Stat for Behavioral Sci I |

Statistics for Behavioral Sciences I
Skill Area II
Prereq.: MATH 101 (C- or higher) or placement exam.
Introductory treatment of research statistics used in
behavioral sciences. Quantitative descriptive statistics,
including frequency distributions, measures of central
tendency and variability, correlation, and regression.
A treatment of probability distributions including binomial
and normal. Introduction to the idea of hypothesis
testing. No credit given to students with credit for STAT
104, 108, 200, 314 or 315.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction All Sections for this Course Mathematics Department Course Attributes: SK2- Mathematics Requirement |

STAT 216 - Stat for Behavioral Sci II |

Statistics for Behavioral Sciences II
Spring.
Skill Area II
Prereq.: STAT 215 or permission of instructor.
Continuation of STAT 215. Survey of statistical tests
and methods of research used in behavioral sciences,
including parametric and nonparametric methods. No
credit given to students with credit for STAT 201, 416
or 453.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: SK2- Mathematics Requirement |

STAT 314 - Intro Stat Secondary Teachers |

Introductory Statistics for Secondary Teachers
Fall.
Prereq.: MATH 218 and 221.
Techniques in probability and statistics necessary for
secondary school teaching. Topics include sampling,
probability, probability distributions, simulation,
statistical inference, and the design and execution of a
statistical study. Computers and graphing calculators
will be used. No credit given to those with credit for
STAT 201, 216 or 453. Graphing calculator required.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 315 - Mathematical Statistics I |

Mathematical Statistics I
Fall.
Prereq.: MATH 221; and MATH 218 or permission of
department chair.
Theory and applications in statistical analysis.
Combinations, permutations, probability, distributions
of discrete and continuous random variables,
expectation, and common distributions (including
normal).
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 416 - Mathematical Statistics II |

Mathematical Statistics II
[GR]
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 425 - Loss/Freq Dist Crdblty Thry |

Loss and Frequency Distributions and Credibility Theory
Spring.
[GR]
Prereq.: STAT 416 (may be taken concurrently).
Topics chosen from credibility theory, loss
distributions, simulation, and time series.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 453 - Applied Statistical Inference |

Applied Statistical Inference
Spring, Summer.
[GR]
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 455 - Experimental Design |

Experimental Design
Fall.
(O)
[GR]
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 456 - Fundamentals of SAS |

Fundamentals of SAS
Spring.
(E)
[GR]
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. No credit given to students with credit
for MKT 444.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 465 - Nonparametric Statistics |

Nonparametric Statistics
Fall.
(E)
[GR]
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 476 - Topics in Statistics |

Topics in Statistics
Spring.
(O)
[GR]
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction All Sections for this Course Mathematics Department Course Attributes: 400 level - Grad Credit |

STAT 520 - Multvriate Anlsis Data Mining |

Multivariate Analysis for Data Mining
Fall.
Prereq.: Two semesters of applied statistics (such as
STAT 104/453, STAT 200/201, or STAT 215/216), or two
semesters of statistics approved by advisor, or permission
of department chair.
Concept-based introduction to multivariate analysis, useful
for data mining and predictive modeling, with emphasis given
to interpreting output and checking model assumptions using
one of the standard statistical packages. Topics may
include: multivariate normal distribution, simultaneous
inferences, one-and two-way MANOVA, multivariate multiple
regression and ANACOVA, correlation, principle component
and factor analysis, discriminant analysis, cluster analysis
and multidimentional scaling, path analysis, structural
equation modeling, and longitudinal data analysis.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 521 - Introduction to Data Mining |

Introduction to Data Mining
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, classification and regression trees, and
report writing.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 522 - Clustering and Affinity Anlsis |

Clustering and Affinity Analysis
Spring.
Prereq.: STAT 521 or permission of department chair.
Investigation and application of methods and models used
for clustering and affinity analysis. Topics may include
dimension reduction methods, k-means clustering,
hierarchical clustering, Kohonen networks clustering, BIRCH
clustering, anomaly detection, market basket analysis, and
association rules using the a priori and generalized rule
induction algoriths.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 523 - Predictive Analytics |

Predictive Analytics
Fall.
Prereq.: STAT 521 or permission of department chair.
Investigation and application of methods and models used for
predictive modeling and predictive analytics. Topics may
include neural networks, logistic regression, k-nearest
neighbor classification, the C4.5 algorithm, CHAID and QUEST
decision trees, feature selection, boosting, naive Bayes
classification and Bayesian networks, time series, and model
evaluation techniques.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 525 - Web Mining |

Web Mining
Spring.
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 526 - Data Mining-Genomics&Proteomcs |

Data Mining for Genomics and Proteomics
Fall.
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, drug discovery, random forests and ensemble
classifiers.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 527 - Text Mining |

Text Mining
Spring.
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, top-down parsing, probability and text sampling,
the bag-of-words model and the effect of sample size.
Extensive use of Perl and Perl modules to analyze text
documents.
4.000 Credit hours 4.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 529 - Current Issues in Data Mining |

Current Issues in Data Mining
Irregular.
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 534 - Appld Catgorcl Data Analsis |

Applied Categorical Data Analysis
Fall.
Prereq.: STAT 201 or STAT 216, or equivalent, or permission
of department chair.
Introduction to analysis and interpretation of categorical
data using analysis of variance or regression analogs.
Topics may include contingency tables, generalized linear
models, logistic regression, log-linear models, models
for matched pairs, and modeling correlated and clustered
responses; use of computer software such as SAS and R.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 551 - Applied Stochastic Processes |

Applied Stochastic Processes
Fall.
(O)
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 567 - Linear Models & Time Series |

Linear Models and Time Series
Spring.
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 570 - Applied Multivariate Analysis |

Applied Multivariate Analysis
Spring.
(O)
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 575 - Mathematical Statistics III |

Mathematical Statistics III
Fall.
(E)
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 576 - Advanced Topics in Statistics |

Advanced Topics in Statistics
Irregular.
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 topic to a maximum of 6 credits.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Independent Study, Lecture, Online Instruction Mathematics Department |

STAT 599 - Thesis |

Thesis
On demand.
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.
3.000 Credit hours 3.000 Lecture hours Schedule Types: Thesis Mathematics Department |