Statistics in Research I. (3-0). Credit 3. For graduate students in other disciplines. A non-calculus exposition of the concepts, methods, and usage of statistical data analysis. T-tests, analysis of variance, and linear regression. Prerequisite: MATH 102 or equivalent.  Credit 3.
Additional Information
*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

Continuation of STAT 651. Concepts of experimental design, individual treatment comparisons, randomized blocks and factorial analysis, multiple regression, chi-square tests and a brief introduction to covariance, non-parametric methods, and sample surveys. Prerequisite: STAT 651.   Credit 3.
Additional Information
*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

The analysis of messy and complex data sets using analysis of variance, analysis of covariance and regression analysis. Transformations; regression diagnostics; nonlinear, robust, logistic and principal components regression; structural equations. Prerequisite: STAT 642 or 652. Credit 3.
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Applied Analytics Using SAS Enterprise Miner:  Introduction to data mining and will demonstrate the procedures; Optimal prediction decisions; comparing and deploying predictive models; neural networks; constructing and adjusting tree models; the construction and evaluation of multi-stage models. NOTE: For this course, you will be required to have a computer with a Windows OS and good processing power.  Prerequisite: STAT 657 and 659 or instructor approval.  Credit 3.
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Programming with SAS/IML, programming in SAS Data Step, advanced use of various SAS procedures. Prerequisite: STAT 604 and STAT 630 or 652. Credit 3.
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Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as SAS, GLIM, SPSSX. Prerequisite: STAT 601 or 652 or 642 or 608 Credit 3.
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