All Current Term Statistics Courses
Spring 2020
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 |
|---|
| 002 | T R
1230-1345
Mitchell Hall 102
| Fletcher Christensen | 3 | 36 | |
| 001 | M W F
0900-0950
Dane Smith Hall 225
| Anastasiia Kim | 3 | 0 | |
| 003 | T R
1400-1515
Dane Smith Hall 127
| Kellin Rumsey | 3 | 0 | |
STAT 428 - Advanced Data Analysis II
A continuation of 427 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminant analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression. Prerequisite: 427.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1530-1645
Collaborative Teaching & Learn 300
| Erik Erhardt | 3 | 33 | Interactive Learning Space |
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 | M W F
1300-1350
Science Math Learning Center 120
| Yan Lu | 3 | 9 | |
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 | M W F
1100-1150
Science Math Learning Center 120
| Yan Lu | 3 | 8 | |
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 |
|---|
| 001 | T R
1400-1515
Dane Smith Hall 334
| Fletcher Christensen | 3 | 5 | |
STAT 479 - Topics in Statistics
Modern topics not covered in regular course offerings.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1100-1215
Dane Smith Hall 129
| Ronald Christensen | 3 | 11 | |
STAT 481 - Intro to 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 |
|---|
| 001 | T R
0930-1045
Science Math Learning Center 352
| Li Li | 3 | 20 | |
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 | 24 | |
STAT 528 - Advanced Data Analysis II
A continuation of 527 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principal components, discriminate analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression. Prerequisite: 527.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1530-1645
Collaborative Teaching & Learn 300
| Erik Erhardt | 3 | 40 | Interactive Learning Space |
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 | M W F
1300-1350
Science Math Learning Center 120
| Yan Lu | 3 | 3 | |
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
1400-1515
Science Math Learning Center 352
| Ronald Christensen | 3 | 7 | |
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 | M W F
1100-1150
Science Math Learning Center 120
| Yan Lu | 3 | 6 | |
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 |
|---|
| 001 | T R
1400-1515
Dane Smith Hall 334
| Fletcher Christensen | 3 | 4 | |
STAT 579 - Sel T:
.
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 | T R
1100-1215
Dane Smith Hall 129
| Ronald Christensen | 3 | 2 | |
STAT 581 - Introduction to Time Series
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 |
|---|
| 001 | T R
0930-1045
Science Math Learning Center 352
| Li Li | 3 | 11 | |
STAT 586 - Nonparametric Curve Estimate
Nonparametric regression, density estimation, filtering, spectral density estimation, image reconstruction and pattern recognition. Tools include orthogonal series, kernels, splines, wavelets and neural networks. Applications to medicine, engineering, biostatistics and economics. Prerequisite: 561. {Offered upon demand}
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 001 | T R
1230-1345
Science Math Learning Center 356
| Guoyi Zhang | 3 | 18 | rescheduled from CRN 52964 STAT 579 003.
|
STAT 649 - Sem Probability & Statistics
(Also offered as MATH 649)
| Section | Time/Location | Instructor | Credits | Seats Open | Notes |
|---|
| 002 |
-
| James Degnan | 1 | 5 | |
