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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.

SectionTime/LocationInstructorCreditsSeats OpenNotes
001M W F
0900-0950
Dane Smith Hall 225
Jared DiDomenico30
003T R
1400-1515
Dane Smith Hall 127
Fred Kaul32
005T R
0930-1045
Dane Smith Hall 228
Patrick Denne35

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1400-1515

Ronald Christensen36

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1100-1215
Science Math Learning Center 352
James Degnan38

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
002M W
1700-1815

315

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.

SectionTime/LocationInstructorCreditsSeats OpenNotes
002T R
1230-1345

Fletcher Christensen317

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
002T R
0930-1045

James Degnan319

STAT 495 - Individual Study

Guided study, under the supervision of a faculty member, of selected topics not covered in regular course offerings.

SectionTime/LocationInstructorCreditsSeats OpenNotes
002James Degnan1 TO 325
003Ronald Christensen1 TO 325
004
-

Erik Erhardt1 TO 325

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1400-1515

Ronald Christensen39

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1100-1215

Ronald Christensen317

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1100-1215
Science Math Learning Center 352
James Degnan38

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
002M W
1700-1815

Yan Lu312

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.

SectionTime/LocationInstructorCreditsSeats OpenNotes

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}

SectionTime/LocationInstructorCreditsSeats OpenNotes
002T R
0930-1045

313

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.

SectionTime/LocationInstructorCreditsSeats OpenNotes
001T R
1400-1515
Mitchell Hall 206
Yan Lu316

STAT 599 - Masters Thesis

Faculty-supervised investigative study that results in the development and writing of a master’s thesis. Offered on a CR/NC basis only.

SectionTime/LocationInstructorCreditsSeats OpenNotes
001Yan Lu1 TO 625
002Ronald Christensen1 TO 625
003Erik Erhardt1 TO 625
004Li Li1 TO 625
005James Degnan1 TO 624
006
-

Guoyi Zhang1 TO 62
007
-

Fletcher Christensen1 TO 625

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.

SectionTime/LocationInstructorCreditsSeats OpenNotes
002F
1200-1250

Guoyi Zhang15

STAT 650 - Reading and Research

A faculty-supervised course culminating in a comprehensive paper or research proposal that integrates knowledge attained through coursework, research, and experience.

SectionTime/LocationInstructorCreditsSeats OpenNotes
001Yan Lu1 TO 625
002James Degnan1 TO 625
003Li Li1 TO 625
004Ronald Christensen1 TO 625
005Li Luo1 TO 625
006Huining Kang1 TO 625
007Guoyi Zhang1 TO 625
008
-

Fletcher Christensen1 TO 623
009
-

1 TO 625

STAT 699 - Dissertation

Faculty-supervised investigative study that results in the development and writing of a doctoral dissertation. Offered on a CR/NC basis only.

SectionTime/LocationInstructorCreditsSeats OpenNotes
002Ronald Christensen3 TO 1225
003Abdullah Mueen3 TO 1225
004Erik Erhardt3 TO 1225
005Li Li3 TO 1225
007James Degnan3 TO 1224
006Yan Lu3 TO 1225
008
-

Gabriel Huerta3 TO 122
009
-

Fletcher Christensen3 TO 1222
001
-

Guoyi Zhang3 TO 125