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Course Criteria
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3.00 Credits
This is the first of a two course sequence in applied statistics. The material covered will provide an introduction to statistical concepts and terminology while focusing on descriptive and inferential methods of data analysis. Topics include descriptive statistics, parameter estimation, tests of significance, confidence intervals, analysis of variance, simple linear regression and correlation. Both parametric and nonparametric methods are presented for the analysis of central tendency, variability, proportions and categorical data.
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3.00 Credits
This is the first of a two course sequence in applied statistics. The material covered will provide an introduction to statistical concepts and terminology while focusing on descriptive and inferential methods of data analysis. Topics include descriptive statistics, parameter estimation, tests of significance, confidence intervals, analysis of variance, simple linear regression and correlation. Both parametric and nonparametric methods are presented for the analysis of central tendency, variability, proportions and categorical data.
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3.00 Credits
This is the second of a two course sequence in applied statistics. The material covered will provide an introduction to the ideas of linear models and experimental design while focusing on methods of data analysis using regression and analysis of variance. Topics include multiple regression analysis, analysis of variance with multiple classification, analysis of covariance, repeated measures analysis of variance, multiple comparison techniques, and diagnostic procedures and transformations. Suitable for students in business administration, economics, and the social, health and biological sciences.
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3.00 Credits
This is the second of a two course sequence in applied statistics. The material covered will provide an introduction to the idea of linear models and experimental design while focusing on methods of data analysis using regression and analysis of variance. Topics include multiple regression analysis, analysis of variance with multiple classification, analysis of covariance, repeated measures analysis of variance, multiple comparison techniques, and diagnostic procedures and transformations. Suitable for students in business administration, economics, and the social, health and biological sciences.
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1.00 - 3.00 Credits
Selected study in a selected area of Statistics.
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3.00 Credits
Estimating and inference from the multivariate normal distribution, Hotelling's T 2, multivariate analysis of variance, multivariate regression, multivariate experimental design, principle component analysis, factor analysis, discriminate analysis and cluster analysis.
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3.00 Credits
Review of probability and statistical inference; binomial, quantile and sign tests; contingency tables; methods based on ranks.
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3.00 Credits
Simple and multiple linear regression, model selection, residual analysis, influence diagnostics, transformation of data to fit assumptions, multicollinearity and an introduction to nonlinear regression.
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3.00 Credits
The study of discrete univariate and multivariate distributions and generating functions, two-way and higher dimensional contingency tables, chi-squared and other goodness-of-fit tests, Cochran-Mantel-Hanzel procedure, binary and multinomial response models, log-linear models, theoretical foundations for the generalized linear models, mixed generalized linear models, longitudinal and spatial data analysis.
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3.00 Credits
Various statistically designed experiments are introduced including randomized blocks designs, Latin squares, incomplete block designs, factorial and fractional factorial designs with and without confounding and nested designs.
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