| Stat 425 [ 3 ] SAS Programming |
| Description: A detailed introduction to the SAS programming language. Topics covered include reading data, storing data, manipulating data, data presentation, graphing, and macro programming. SAS software will be used. |
| Prerequisite: Stat 345, 427. |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| 35547 |
Stat 425 001 |
W |
1730-2000 |
ESCP |
|
Sorell, M.
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| Stat 427 [ 3 ] Advanced Data Analysis I |
| Description: Statistical tools for scientific research, including parametric and non-parametric methods for ANOVA and group comparisons, simple linear and multiple linear regression and basic ideas of experimental design and analysis. Emphasis placed on the use of statistical packages such as Minitab and SAS. Course cannot be counted in the hours needed for graduate degrees in Mathematics and Statistics. |
| Prerequisite: Stat 145 |
| Semesters offered: Fall |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| |
Stat 427 001 |
MWF |
1100-1150 |
ANTHO |
|
Cancelled, .
|
| AOA Stat 527-001 |
|
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| |
Stat 427 002 |
MWF |
1400-1450 |
DSH |
|
Cancelled, .
|
| AOA Stat 527-002 |
|
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|
|
| 35812 |
Stat 427 003 |
TR |
1100-1215 |
DSH |
|
Christensen, R.
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| Stat 428 [ 3 ] Advanced Data Analysis II |
| Description: A continuation of 527 that focuses on methods for analyzing multivariate data and categorical data. Topics include MANOVA, principle components, discriminant analysis, classification, factor analysis, analysis of contingency tables including log-linear models for multidimensional tables and logistic regression. |
| Prerequisite: 527 |
|
| Stat 434 [ 3 ] Contingency Tables & Dependence Structures |
| Description: This course examines the use of log-linear models to analyze count data. It also uses graphical models to examine dependence structures for both count data and measurement data. |
| Prerequisite: 345, 527 |
|
| Stat 440 [ 3 ] Regression Analysis |
| Description: Simple regression and multiple regression. Residual analysis and transformations. Matrix approach to general linear models. Model selection procedures, nonlinear least squares, logistic regression. Computer applications. |
| Prerequisite: 427 |
| Semesters offered: Fall |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| 13679 |
Stat 440 001 |
TR |
1230-1345 |
DSH |
|
Storlie, C.
|
| AOA Stat 540 |
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| Stat 445 [ 3 ] Analysis of Variance and Experimental Design |
| Description: 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 |
| Semesters offered: Spring |
|
| Stat 453 [ 3 ] Statistical Inference with Applications |
| Description: 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 |
| Semesters offered: Spring |
|
| Stat 461 [ 3 ] Probability |
| Description: (Also offered as MATH 441.) Mathematical models for random experiments, random variables, expectation. The common discrete and continuous distributions with application. Joint distributions, conditional probability and expectation, independence. Laws of large numbers and the central limit theorem. Moment generating functions. |
| Prerequisite: Math 264 |
| Semesters offered: Fall |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| 13690 |
Stat 461 001 |
TR |
1400-1515 |
ORTG |
|
Guindani, M.
|
| AOA Stat 561, Math 441 |
|
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| Stat 469 [ 3 ] Topics in Probability |
| Description: May be repeated for credit, no limit. |
| Semesters offered: Offered Upon Demand |
|
| Stat 470 [ 3 ] Industrial Statistics |
| Description: Basic ideas of statistical quality control and improvement. Topics covered: Demings 14 points and deadly diseases, Pareto charts, histograms, cause and effect diagrams, control charts, sampling, prediction, reliability, experimental design, fractional factorials, Taguchi methods, response surfaces. |
| Prerequisite: Stat 345 |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| 33788 |
Stat 470 001 |
MWF |
1400-1450 |
DSH |
|
Zhang, G.
|
| AOA Stat 570 |
|
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| Stat 472 [ 3 ] Sampling Theory and Practice |
| Description: 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: Stat 345 |
| Semesters offered: Alternate Falls |
|
| Stat 474 [ 3 ] Biostatistical Methods: Survival Anal. & Log. Regression |
| Description: A detailed overview of methods commonly used to analyze medical and epidemiological data. Topics include the Kaplan-Meier estimate of the survivor function, models for censored survival data, the Cox proportional hazards model, methods for categorical response data including logistic regression and probit analysis, generalized linear models. |
| Prerequisite: 528 or 540 |
|
| Stat 476 [ 3 ] Multivariate Analysis |
| Description: Tools for multivariate analysis including multivariate ANOVA, principle components analysis, discriminant analysis, cluster analysis, factor analysis, structural equations modeling, canonical correlations and multidimensional scaling. |
| Prerequisite: 528 or 540 |
| Semesters offered: Offered upon demand |
| Fall 2009 Class Schedule |
| Call # |
Course |
Days |
Time |
Bldg. |
Room |
Instructor |
| 33790 |
Stat 476 001 |
TR |
0930-1045 |
DSH |
|
Storlie, C.
|
| AOA Stat 576 |
|
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| Stat 477 [ 3 ] Introduction to Bayesian Modeling |
| Description: 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: Stat 461 and (427 or 440). |
| Semesters offered: Alternate Springs |
|
| Stat 479 [ (3, n ] Topics in Statistics |
| Description: Modern topics not covered in regular course offerings. |
|
| Stat 481 [ 3 ] Introduction to Time Series Analysis |
| Description: 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 |
| Semesters offered: Alternate Springs |
|
| Stat 485 [ 3 ] Nonparametric & Robust Methods |
| Description: Statistical methods that are insensitive to the distribution of the data. Sign tests, Kolmogorov-Smirnov tests, rank tests including the Wilcoxon, Mann-Whitney, Kruskal-Wallis, and Friedman tests. Robust estimation including M estimators, L estimators, and R estimators. |
| Prerequisite: 461 or permission of instructor |
| Semesters offered: Offered Upon Demand |
|
| Stat 495 [ (1-3 ] Individual Study |
| Description: (Credit: 1-3 to a maximum of 6.) Guided study, under supervision of a faculty member, of selected topics not covered in regular course offerings. (max. of 6 credits) |
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