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Course Criteria
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3.00 Credits
Method of least squares, simple and multiple linear regression, polynomial regression, analysis of residuals, dummy variables, and model building.
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3.00 Credits
Factorial designs, balanced and unbalanced data, fixed and random effects, randomized blocks, Latin squares, analysis of covariance, and multiple comparisons.
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3.00 Credits
One-sample test, tests of 2 or more related or independent samples, measures of correlation, and tests of trend and dependence.
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3.00 Credits
Discriminant analysis, principal components, factor analysis, cluster analysis, and canonical correlation.
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3.00 Credits
Models for discrete and count data, measures of association, and log-linear and regression models for contingency tables.
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3.00 Credits
Simple random, stratified, cluster sampling; variance estimation in complex surveys; nonparametric superpopulation approaches; nonresponse models; computational methods.
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1.00 - 12.00 Credits
Structured practical experience following a contract or plan, supervised by faculty and practitioners.
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1.00 - 12.00 Credits
Independent study in which a student meets regularly with a faculty member to discuss assignments. Course may include such assignments as intensive reading in a specialized area, writing a synthesis of literature on a specified topic, or writing a literature review of a topic.
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1.00 - 12.00 Credits
A small class emphasizing discussion, presentations by students, and written research papers.
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1.00 - 12.00 Credits
Independent study in which a student, under the supervision of a faculty member, conducts research that is expected to lead to a specific project such as a thesis or dissertation, report, or publication. Assignments might include data collection, experimental work, data analysis, or preparation of a manuscript.
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