All Next Term Statistics Courses
Spring 2024
This data is offered for your convenience only. The schedule data is updated regularly and may not reflect recent changes to the Schedule of Classes. For full, up-to-date course information please contact the Registrar's office
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STAT 345 - Elements of Math Stat & Prob
An introduction to probability including combinatorics, Bayes' theorem, probability densities, expectation, variance and correlation. An introduction to estimation, confidence intervals and hypothesis testing.
Prerequisite: MATH 1440 or MATH 1522.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | M W F
0900-0950
Dane Smith Hall 225
| Jared DiDomenico | 3 | 0 | |
| 003 | T R
1400-1515
Dane Smith Hall 127
| Fred Kaul | 3 | 2 | |
| 005 | T R
0930-1045
Dane Smith Hall 228
| Patrick Denne | 3 | 5 | |
STAT 445 - Analysis of Variance Design
A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics. Prerequisite: 440. {Spring}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1400-1515
| Ronald Christensen | 3 | 6 | |
STAT 453 - Statistical Inference with App
Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including student's t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness). Prerequisite: 461. {Spring}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1100-1215
Science Math Learning Center 352
| James Degnan | 3 | 8 | |
STAT 472 - Sampling Theory & Practice
Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies. Prerequisite: **345. {Alternate Falls}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | M W
1700-1815
| | 3 | 15 | |
STAT 477 - Intro Bayes Modeling
An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes' theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis.
Prerequisite: (427 or 440) and 461.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | T R
1230-1345
| Fletcher Christensen | 3 | 17 | |
STAT 481 - Intro Time Series Analysis
Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control. Prerequisite: 461. {Alternate Springs}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | T R
0930-1045
| James Degnan | 3 | 19 | |
STAT 495 - Individual Study
Guided study, under the supervision of a faculty member, of selected topics not covered in regular course offerings.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | | James Degnan | 1 TO 3 | 25 | |
| 003 | | Ronald Christensen | 1 TO 3 | 25 | |
| 004 |
-
| Erik Erhardt | 1 TO 3 | 25 | |
STAT 545 - Analysis of Variance Design
A data-analytic course. Multifactor ANOVA. Principles of experimental design. Analysis of randomized blocks, Latin squares, split plots, etc. Random and mixed models. Extensive use of computer packages with interpretation, diagnostics. Prerequisite: 540. {Spring}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1400-1515
| Ronald Christensen | 3 | 9 | |
STAT 547 - Multivariate Analysis
Hotelling T2, multivariate ANOVA and Regression, classification and discrimination, principal components and factor analysis, clustering, graphical and computational techniques, topics in linear models. Prerequisite: 546. {Alternate Springs}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1100-1215
| Ronald Christensen | 3 | 17 | |
STAT 553 - Statistical Inference with App
Transformations of univariate and multivariate distributions to obtain the special distributions important in statistics. Concepts of estimation and hypothesis testing in both large and small samples with emphasis on the statistical properties of the more commonly used procedures, including Students t-tests, F-tests and chi-square tests. Confidence intervals. Performance of procedures under non-standard conditions (i.e., robustness). Prerequisite: 561. {Spring}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1100-1215
Science Math Learning Center 352
| James Degnan | 3 | 8 | |
STAT 572 - Sampling Theory & Practice
Basic methods of survey sampling; simple random sampling, stratified sampling, cluster sampling, systematic sampling and general sampling schemes; estimation based on auxiliary information; design of complex samples and case studies. Prerequisite: **345. {Alternate Falls}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | M W
1700-1815
| Yan Lu | 3 | 12 | |
STAT 577 - Intro Bayseian Modeling
An introduction to Bayesian methodology and applications. Topics covered include: probability review, Bayes' theorem, prior elicitation, Markov chain Monte Carlo techniques. The free software programs WinBUGS and R will be used for data analysis.
Prerequisite: (527 or 540) and 561.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
STAT 581 - Intro Time Series Analysis
Introduction to time domain and frequency domain models of time series. Data analysis with emphasis on Box-Jenkins methods. Topics such as multivariate models; linear filters; linear prediction; forecasting and control. Prerequisite: 561. {Alternate Springs}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | T R
0930-1045
| | 3 | 13 | |
STAT 590 - Statistical Computing
A detailed examination of essential statistical computing skills needed for research and industrial work. Students will use S-Plus, Matlab and SAS to develop algorithms for solving a variety of statistical problems using resampling and simulation techniques such as the bootstrap, Monte Carlo methods and Markov chain methods for approximating probability distributions. Applications to linear and non-linear models will be stressed. Prerequisite: 528.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1400-1515
Mitchell Hall 206
| Yan Lu | 3 | 16 | |
STAT 649 - Sem Probability & Statistics
(Also offered as MATH 649)
Course emphasizes investigation, evaluation, and discussion of areas of specialized knowledge or inquiry relevant to the profession or field of study.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | F
1200-1250
| Guoyi Zhang | 1 | 5 | |
