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Course Criteria
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3.00 Credits
Topics include hypothesis testing using data from unknown distributions, tests of independence, tests of goodness-of-fit, rank correlation, and simple linear regression. Prerequisite: STAT 3233 or equivalent.
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3.00 Credits
Statistical methods and data analysis with SAS programming. Point estimation, interval estimation and tests of significance for comparing two population means and proportions. Power analysis and choosing sample size. Nonparametric methods and Chi square tests. Linear regression, analysis of variance and multiple comparison procedures. Other topics as time permits. Prerequisite, STAT 3233.
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3.00 Credits
Topics include the bivariate normal distribution, multiple and partial correlation, canonical correlation, discriminant analysis, multivariate analysis of variance (MANOVA), and factor analysis. Prerequisite: STAT 3233, MATH 3243, or equivalent.
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3.00 Credits
Simple and multiple linear regression, related estimation and hypothesis testing, model selection, examination of residuals, multicollinearity diagnostics, outliers and influence, qualitative predictor variables, logistic regression. Prerequisite: STAT 3233 or equivalent.
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3.00 Credits
Single-factor, twofactor, and multifactor analysis of variance (ANOVA), related estimation and hypothesis testing, multiple comparison procedures, random effects models, analysis of covariance, ANOVA in some standard experimental designs. Prerequisite: STAT 6653 or permission of instructor.
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3.00 Credits
Replication, randomization, and blocking, analysis of variance, full and factorial experiments at two and three levels, effect aliasing, design resolution and minimum aberration criteria, nonregular designs and complex aliasing, introduction to response surface methodology. Prerequisite: STAT 3233 or equivalent.
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3.00 Credits
Applications of elementary statistics. Advanced principles including statistical study, data gathering, variance and covariance. Prerequisite, STAT 4453.
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3.00 Credits
A continuation of STAT 6703.
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3.00 Credits
Abstract probability spaces, random variables, conditional probability, independence, types of convergence, sums of independent random variable, stochastic processes. Prerequisite: MATH 4553.
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3.00 Credits
Applications of advanced statistical techniques to the Life and Health Sciences. Topics include estimation and hypothesis testing, single and multiway analysis of variance (ANOVA), linear regression, correlation and frequency analysis. Prerequisite: an introductory level statistics course.
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