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ST 150 Contemporary Mathematics and Statistics Seminar 1 cr
This course gives an overview of modern mathematics and statistics
from the point of view of the practitioners. The course is designed
for majors in mathematics and statistics at all levels as well as
those students who are considering mathematics and statistics as a
major or minor area of study. Topics usually included are elements
of geometry, algebra, analysis, methods of statistical inference,
the role of the computer in the analytical sciences; these topics
vary from semester to semester. This course cannot be taken for credit
simultaneously with MA 150, but may be repeated in different semesters.
NOTE: May be offered for Honors Credit.
ST 210 Statistical Reasoning and Applications 3 cr
An introduction to modern statistics designed to provide the student
with a solid foundation in statistical concepts, reasoning and applications.
Emphasis given to problem identification, methodology selection and
interpretation of results. Analysis of data accomplished by extensive
use of statistical computer software, thereby minimizing manual computation.
Coverage includes descriptive statistics, probability models, estimation,
hypothesis testing, design of experiments and analysis of variance
(ANOVA), linear regression and correlation. Prerequisite: High School
level algebra is recommended. NOTE: ST 210 is intended for students
in all disciplines except Engineering, Computer Science, and Mathematics.
NOTE: May be offered for Honors Credit.
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. Prerequisite: ST 210 or 315
or 320.
ST 340 Design and Analysis of Experiments 3 cr
Principles, constructions, and analysis of experimental designs to
include completely randomized, 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. Prerequisite: ST 210 or 315 or 320.
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. Prerequisite: ST
210 or 315 or 320.
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. Prerequisite:
ST 310 or 315 or 320 or 335.
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. Prerequisite: ST 210 or 315 or 320.
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.
Prerequisite: Any 300 level ST course.
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. Prerequisite: MA 237 and ST 335 or 340.
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. Prerequisite: Any 300
level ST course.
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. Prerequisite: Any 300 level ST course.
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) 3 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. Prerequisite: Approval
of department chair.
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 499 Honors Senior Project 3-6 cr
With the guidance and advice of a faculty mentor, Honors Students
will identify, and carry out a research project in Statistics. The
outcome of the research project will include a formal presentation
at the annual Honors Student Colloquium. The senior project will be
judged and graded by three members of the faculty, chaired by the
faculty mentor.
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.
Prerequisite: ST 540.
ST 550 Environmental Statistics 3 cr
Sampling environmental populations, para- metric 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. Prerequisite: ST 540.
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