NOTE: ST 210 is intended for students in all disciplines except Engineering, Computer
Science, and Mathematics.

ST 310 Statistical Research Techniques 3 cr
Continuation of ST 210 providing a more rigorous treatment of methodologies introduced
in ST 210. Additional coverage will be given to experimental design, analysis of variance
(ANOVA), regression, model building, nonparametric techniques, contingency table analysis,
sampling and survey methods, and statistical simulations. Statistical computer software will be
extensively used for data analysis. Prerequisite: ST 210.

NOTE: Credit for only ONE course from ST 310, ST 315 and ST 320 is allowed.

ST 315 Applied Probability and Statistics 3 cr
Concepts of probability theory, discrete and continuous probability distributions
including gamma, beta, exponential and Weibull, descriptive statistics, sampling, estimation,
confidence intervals, testing of hypothesis, ANOVA and multiple comparisons, linear and
multiple regression, correlation, nonparametric analysis, contingency table analysis,
computer-assisted data analysis using appropriate statistical software. Prerequisite: MA 125.

NOTE: ST 315 and ST 320 are intended for students in Engineering, Computer Science,
and Mathematics. ST 315 covers additional probability distributions while ST 320 additionally
covers concepts of quality control and acceptance sampling. Students in these disciplines should
consult with their academic advisor for appropriate choice between ST 315 and ST 320. Credit
for only ONE course from ST 310, ST 315 and ST 320 is allowed.

ST 320 Applied Statistical Analysis 4 cr
Descriptive statistics, probability distributions, sampling, estimation, confidence intervals
and hypothesis testing, experimental designs, ANOVA and multiple comparisons, linear and
multiple regression, correlation, nonparametric analysis, goodness of fit, contingency table
analysis, quality control, acceptance sampling, computer-assisted data analysis using appropriate
statistical software. Prerequisite: MA 125.

NOTE: ST 315 and ST 320 are intended for students in Engineering, Computer Science,
and Mathematics. ST 315 covers additional probability distributions while ST 320 additionally
covers concepts of quality control and acceptance sampling. Students in these disciplines should
consult with their academic advisor for appropriate choice between ST 315 and ST 320.
NOTE: Credit for only ONE course from ST 310, ST 315 and ST 320 is allowed.

ST 335 Applied Regression Analysis 3 cr
Simple, polynomial and multiple linear regression; residual and lack-of-fit analysis;
simple, multiple, partial and multiple-partial correlation analysis; model building algorithms,
dummy variables; analysis of covariance; model comparisons; analysis of experimental designs
including messy data; nonlinear regression models; computer-assisted data analysis using
appropriate statistical software.
ST 340 Design and Analysis of Experiments 3 cr
Principles, constructions, and analysis of experimental designs to include completely
ramdomized, randomized complete block, latin square and split plot designs, factorial
experiments, designs with nested and/or crossed factors, multifactor experiments with
randomization restrictions, transformations, incomplete block designs, multiple comparisons
including contrasts, confounding, fractional replication, computer-assisted data analysis.
ST 345 Sampling and Survey Techniques 3 cr
Sampling concepts and designs for survey investigations; sampling methodologies
including applications of simple random, stratified, one- and two-stage cluster, and systematic
sampling; sample size determination; ratio and regression estimation; population size estimation;
random response modeling; acceptance sampling including applications of single and multiple
2-class attribute sampling plans; computer-assisted data analysis using appropriate statistical
software.
ST 350 Applied Time Series Analysis 3 cr
Fundamentals concepts; classical regression models as forecasting models, exponential
smoothings, stationary and nonstationary models, additive and multiplicative decompositions,
moving average, autoregressive, ARMA and ARIMA processes, estimation in MA, AR, ARMA
and ARIMA processes. Box-Jenkins methodology, computer aided modeling, applications.
ST 355 Nonparametric Statistical Methods 3 cr
Distribution-free analysis of location and scale measures, non-parametric treatment of
fundamental statistical designs, nonparametric comparison procedures, association and
contingency table analysis, nonparametric goodness-of-fit procedures, and tests for randomness,
nonparametric regression and other measures of association, computer intensive statistical
methods.
ST 415 Statistical Quality Control and Reliability 3 cr
Probability distributions in quality control, inferences about process quality, control
charts for attributes and variables, process capability analysis, economic design of control charts,
cusum charts, acceptance sampling by attributes and variables, reliability concepts, censoring,
definitions and properties of survival distributions, methods of estimating and comparing
reliability distributions, Kaplan-Meier estimation, burn-in models with a major emphasis on
computer-assisted data analysis.
ST 425 Applied Linear Models 3 cr
Some results of matrix algebra, multivariate normal distributions, distributions of
quadratic forms, general linear models, design models with one factor and two factors including
interaction, component-of-variance models, computing techniques.
ST 450 Categorical Data Analysis 3 cr
Analysis of two-way, three-way and higher dimensional contingency tables using
log-linear models, measures of association for nominal and ordinal tables, multiple-factor
models, multiple response models, logistic regression, weighted least squares.
ST 460 Multivariate Statistical Analysis 3 cr
Multivariate normal distribution, sampling distribution, hypothesis testing, principal
components and introduction to factor analysis, canonical correlation analysis, discriminant and
classification analysis, MANOVA.
ST 470 Theory of Statistics 3 cr
A comprehensive introduction to the mathematical foundations of statistics. Sufficient
statistics and information. Parameter estimation, maximum likelihood and moment estimation,
optimality properties of estimators and confidence intervals. Hypothesis testing, likelihood ratio
tests and power functions. Credit for both ST 470 and MA 551 is not allowed. Prerequisite: MA
451 or MA 550.
ST 480 Statistical Practicum (W) 1 cr
Relates to the student's classroom studies with actual statistical problems encountered in
practice. Working with the departmental statistical consultant, the student will participate in
providing statistical assistance to research faculty in applied fields.
ST 490 Special Topics 1-3 cr
Selected topics in advanced undergraduate applied statistics. This course may be repeated
for a maximum of six credits.
ST 494 Directed Studies 1-3 cr
Directed study. May be repeated for a maximum of six credits. Prerequisite: Permission
of the department chair.
ST 540 Statistics in Research I 3 cr
A service course for graduate students in disciplines other than mathematics and
statistics. A non-calculus exposition in support of application. Coverage includes descriptive
statistics, probability and probability distributions, sampling, estimation, tests of significance,
analysis of variance, correlation, linear, polynomial, and multiple linear regression including
residual and lack of fit analysis, nonparametric procedures, contingency table analysis, and
computer assisted data analysis using appropriate computer software.
ST 545 Statistics in Research II 3 cr
Continuation of ST 540. Coverage includes regression analysis through matrices,
multiple, partial and multiple-partial correlation analysis, model building algorithms, non-linear
regression, analysis of covariance, completely randomized, randomized complete block, and
factorial experimentation for equal and unequal cell replication, logistic regression, resampling,
basic multivariate techniques, and computer assisted data analysis.
ST 550 Environmental Statistics 3 cr
Sampling environmental populations, parametric and nonparametric estimation;
applications of lognormal, Weibull, gamma and beta distributions; locating hot spots; censored
data; outlier detection; trend analysis, seasonality; estimation of animal abundance.

Department of Mathematics and Statistics

College of Arts and Sciences