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Course Criteria
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3.00 Credits
Multivariate regression analysis with emphasis on application using a statistical software package. Topics include method of least squares, residual analysis, colinearity, data transformation, polynomial regression, general linear model, selecting a best regression model, and logistic regression. Offered fall semesters on sufficient demand. Prerequisites: STA 216.
Prerequisite:
Prerequisites: STA 216.
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3.00 Credits
An introduction to applications and the conceptual framework for predictive analytics and modeling, using a statistical programming language such as R. Topics include preparing data for predictive modeling, exploratory data analysis and visualizations, multiple linear regression, logistic regression for classification, and methods for model selection and evaluation. Offered winter semester. Prerequisite: STA 215 or STA 312.
Prerequisite:
Prerequisite: STA 215 or STA 312.
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3.00 Credits
An examination of statistics reported in the media. Students will read news stories and published research to critically evaluate the conclusions made, recognizing when assertions are and are not supported by evidence. Common fallacies and misconceptions will be covered. Part of the Information, Innovation, or Technology Issue. Offered fall and winter semesters. Prerequisites: STA 215 and junior standing.
Prerequisite:
Prerequisites: STA 215 and junior standing.
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3.00 Credits
An application-oriented overview of procedures and techniques for the collection, evaluation, and analysis of demographic data. Topics include sources of, and problems with, vital statistics data, data registries and surveys, measures of population growth, composition, fertility, mortality, and migration. Part of the Globalization Issue. Offered winter semester. Prerequisites: STA 215 or STA 312; junior standing.
Prerequisite:
Prerequisites: STA 215 or STA 312; junior standing.
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3.00 Credits
An application-oriented overview of the statistical methodology that can be utilized to describe and evaluate the performance of individuals or teams participating in sports. Emphasis will be on data collection, descriptive statistics, and statistical inference and modeling utilized in sports. Part of the Information, Innovation, or Technology Issue. Offered fall and winter semesters. Prerequisite: Junior standing and either STA 215 or STA 312.
Prerequisite:
Prerequisite: Junior standing and either STA 215 or STA 312.
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1.00 - 9.00 Credits
Readings, lectures, discussions, and/or lab (or any combination) in specific statistics topics. Permission of the instructor required. Offered on sufficient demand. Prerequisites: Dependent upon the topic selected.
Prerequisite:
Prerequisites: Dependent upon the topic selected.
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1.00 - 6.00 Credits
Of varying focus, the course makes use of the history, culture, and society of a host country in order to highlight disciplinary perspectives in context. To be taught in that country (or countries) as part of an apporved study abroad program. By permit only. Credit may vary.
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4.00 Credits
A theoretical study of the following topics: sample space, conditional probability, independence, Bayes' Theorem, Bernoulli Trials, and discrete and continuous random variables and their distributions, Chebyshev's inequality, joint distribution, expectation, variance, and moment generating functions. Offered fall semester. Prerequisites: (STA 215 or STA 312) and MTH 202.
Prerequisite:
Prerequisites: (STA 215 or STA 312) and MTH 202.
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4.00 Credits
A theoretical study of the following topics: the Law of Large Numbers, the Central Limit Theorem, the nature of statistical inference, tests of hypotheses, sampling theory, point and interval estimation, linear models, analysis of categorical data, and distribution-free methods. Offered winter semester. Prerequisites: STA 412 and MTH 204.
Prerequisite:
Prerequisites: STA 412 and MTH 204.
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3.00 Credits
An introduction to statistical programming and graphics using the object-oriented statistical language R. Skills in writing R code to perform statistical analyses, graphics and simulations are developed. Emphasis will be on solving real problems with hands-on work including randomization statistics, time series, data mining and big data analysis. Cross-listed with STA 518. Offered fall semester. Prerequisites: (STA 215 or STA 220 or STA 312) and (STA 216 or CIS 162).
Prerequisite:
Prerequisites: (STA 215 or STA 220 or STA 312) and (STA 216 or CIS 162).
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