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Course Criteria
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3.00 Credits
This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and applications.
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3.00 Credits
The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Prerequisites: Introductory statistics course. The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Prerequisites: Introductory statistics course.
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1.00 - 4.00 Credits
This course provides the opportunity to offer a new topic in the subject area of Statistics.
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1.00 Credits
Enrollment in STAT LAB (3980) is required for all students in the department’s 3000 level appled statistics courses (STAT 3080, 3220, 3430, 3130). STAT 398 may be repeated for credit provided that a student in enrolled in at least one of these 3000-level applied courses; however, no more than one unit os STAT 3980 may be taken in any semester.
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1.00 - 3.00 Credits
Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory. Students will work together with a graduate student consultant.
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3.00 Credits
Introduces estimation and hypothesis testing in applied statistics, especially the medical sciences. Measurement issues, measures of central tendency and dispersion, probability, discrete probability distributions (binomial and Poisson), continuous probability distributions (normal, t, chi-square, and F), and one- and two-sample inference, power and sample size calculations, introduction to non-parametric methods, one-way ANOVA and multiple comparisons.
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3.00 Credits
Introduces statistical computing using S-PLUS. Topics include descriptive statistics for continuous and categorical variables, methods for handling missing data, basics of graphical perception, graphical displays, exploratory data analysis, the simultaneous display of multiple variables. Students should be experienced with basic text-editing and file manipulation on either a PC or a UNIX system, and with either a programming language (e.g. BASIC) or a spreadsheet program (e.g. MINITAB or EXCEL). Credit earned in this course cannot be applied toward a graduate degree in statistics.
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3.00 Credits
A calculus based introduction to the principles of statistical inference. Topics include sampling theory, point estimation, confidence intervals, hypothesis testing. Additional topics such as nonparametric methods or Bayesian statistics. May not be used for graduate degrees in Statistics. May not be taken if credit has been received for STAT 3120. Prerequisites: MATH 3100 or 5100 or consent of instructor.
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3.00 Credits
Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression.
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3.00 Credits
Topics include matrix algebra, random sampling, multivariate normal distributions, multivariate regression, MANOVA, principal components, factor analysis, discriminant analysis. Statistical software, such as SAS or S-PLUS, will be utilized.
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