| STATISTICS (ST) |
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| ST
150 |
Contemporary
Mathematics |
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. |
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| NOTE: May be offered for Honors
Credit. |
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| ST 210 |
Statistical Reasoning and
Applications (C) |
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. Computer Lab fee.
|
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| NOTE: ST 210 is intended for students in all disciplines
except Engineering and Computer Science. |
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| NOTE: May be offered for Honors Credit. |
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| 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, time series analysis and statistical
simulations. Statistical computer software will be extensively used for
data analysis. Prerequisite: C or better
in ST 210. Computer Lab fee. |
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| NOTE: Credit for only ONE course from ST 310, ST 315 and ST
320 is allowed. |
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| 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: C or better in MA
125. Computer Lab fee. |
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| 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: C or better
in MA 125. Computer Lab fee. |
| |
| 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. |
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| NOTE: Credit for only ONE course from ST 310,
ST 315 and ST 320 is allowed. |
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| 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: C or better
in one of ST 210, ST 315, or ST 320.
Computer Lab fee. |
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|
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: C
or better in ST 210 or ST 315 or ST
320. Computer Lab fee. |
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| 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: C or better in ST 210
or ST 315 or ST320. Computer Lab fee. |
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| 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: C or better in ST
310 or ST 315 or ST 320 or ST 335. Computer Lab fee. |
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| 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: C or better in ST
210 or ST 315 or ST 320. Computer Lab fee. |
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| 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, six sigma concepts,
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: C or better in Any 300
level ST course. Computer Lab fee. |
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| 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: C or better in MA
237 and ST 335 or ST 340. Computer Lab fee. |
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| 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: C or better in any
300 level ST course. Computer Lab fee. |
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| 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: C or better in any 300
level ST course. Computer Lab fee. |
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| 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: C or better in MA
451 or MA 550. |
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| ST 475 |
Statistical Computing and Graphics |
3 cr |
| Introduction to computer-assisted data analysis with statistical
computer software, including SAS, R/S-Plus. Coverage includes
basics of SAS, common SAS
statistical procedures, high-dimensional data visualization, some
elements of statistical computing such as numerical computation,
semi-numerical computation, symbolic and graphical computation, and
special topics selected by instructor. (Credit for both ST 475 and ST
575 is not allowed. Prerequisite: C or better
in ST 210 or ST 315 or permission of
instructor.) |
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| 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. Prerequisite: Approval of
department chair. Computer Lab fee. |
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| 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. |
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| ST 494 |
Directed Studies |
1-3 cr |
|
| Directed study. May be repeated for a
maximum of six credits. Prerequisite: Permission of the department
chair. |
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| 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. |
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| 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. Computer lab fee. |
| |
| 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. Computer lab fee. |
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| 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. Prerequisite: C or better
in ST 540. Computer lab
fee. |
| |
| ST 575 |
Statistical Computing and Graphics |
3 cr |
| Introduction to computer
assisted data analysis with statistical computer software, including SAS,
R/S-Plus. Coverage includes basics of SAS, common SAS
statistical procedures, high-dimensional data visualization, some
elements of statistical computing such as numerical computation,
semi-numerical computation, symbolic and graphical computation, and
special topics selected by instructor. (Credit for both ST 475 and ST
575 is not allowed. Prerequisite: C or better
in ST 210 or ST 315 or permission of
instructor). |
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College of Arts and Sciences
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