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Course Criteria
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1.00 - 3.00 Credits
Topics vary.
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1.00 - 6.00 Credits
Supervised off-campus experience in application of statistical principles and methods in business, industry, or government, culminating in a written and oral report.
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2.00 - 6.00 Credits
Faculty directed reading and investigation of specified topic in probability or statistics culminating in a written report.
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3.00 Credits
Intended as an alternative to 201 for higher GPA students. ( QR) Contact Hour Distribution: Two 50-minute lectures and one 110-minute lab per week. (RE) Prerequisite(s): Mathematics 125 or Mathematics 141. Recommended Background: 28 composite ACT or 1250 composite SAT.
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3.00 Credits
Data collection and descriptive statistics. Concepts of probability and probability distributions. Discrete and continuous distributions. Estimation of means, confidence intervals, and hypothesis tests for single mean and proportion. Simple regression and correlation. Process improvement, statistical process control, and 2-level experiments. Use of statistical computing software. (RE) Prerequisite(s): Mathematics 142 or Mathematics 148.
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3.00 Credits
Simple linear regression and correlation analysis, time series analysis, multiple regression, variable selection, regression diagnostics, partial correlation, and categorical data analysis techniques. Use of statistical computing software. Applied course appropriate for a general audience. (RE) Prerequisite(s): 201 or 251.
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3.00 Credits
Strategies of experimentation: randomization, blocking, sequential experimentation, replication. Design and analysis of experiments to collect nominal data (paired comparison, triangle tests), ordinal data (rating and ranking experiments) and numerical data (single and multiple factor experiments, fractional factorials). Use of statistical computing software. Applied course for a general audience. (RE) Prerequisite(s): 201 or 251.
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3.00 Credits
Concept of special versus common causes of variation. Construction and interpretation of control charts for attributes and variables data, Pareto charts, cause/effect diagrams, and process flow diagrams. Rational subgrouping issues. Process capability analysis and capability indices. Statistical tolerancing. Accuracy, precision and resolution of measurement processes. Quantifying components of variation. Introduction to design of experiments. Discussion of enumerative versus analytical statistical techniques. (RE) Prerequisite(s): 201 or 251.
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3.00 Credits
Numeric and graphic description of data, probability and probability distributions, simulation, and sampling distributions. Estimation and hypothesis testing for one and two samples, parametric and nonparametric approaches, and bootstrapping. Tests for count data, simple and multiple linear regression, diagnostics and validation, and analysis of variance. Uses SAS and other statistical software. (RE) Prerequisite(s): 320.
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3.00 Credits
Linear regression and correlation, multiple regression, polynomial regression, selection of variables, use of dummy variables, and analysis of residuals. Logistic regression and its applications. Matrix formulation of model. Use of standard computer packages. Major writing requirement. (RE) Prerequisite(s): 320.
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