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Course Criteria
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3.00 Credits
This is the third course required for the Online Certificate in Applied Statistics - a Certificate for working professionals. This methods course will teach students the core statistical tests any practitioner would be expected to use including ChiSquare, ANOVA, Post Hoc Testing, Correlation, Basic Regression and Non-Parametric Methods.
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3.00 Credits
This is the fourth course required for the Online Certificate in Applied Statistics - a Certificate for working professionals. This course will provide students with advanced training in the most widely used statistical modeling technique - regression. Students will understand assumptions necessary to execute regression and how to address violations of assumptions including normality, homoscedasticity and multi-collinearity.
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3.00 Credits
This is the fifth and final course required for the Online Certificate in Applied Statistics - a Certificate for working professionals. This course will provide students with advanced training in logistic regression (binary response modeling) and in discriminant analysis (multi-level response modeling).
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3.00 Credits
Prerequisite: MATH 1107 or CSIS 2300 or ECON 2300. Introduction to the use of computer-based statis-tical software packages and applications in the analysis and interpretation of the data. Topics include both descriptive statistics and inference methods. Software packages include SAS, JMP, SPSS, Minitab, and EXCEL.
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3.00 Credits
Prerequisite: STAT 3010. This course is designed to provide students with a foundation in statistical methods, including review of descriptive statistics, the normal distri-bution, non-normal distributions (e.g., binomial, poisson, uniform), monotonic transformations, hypothesis testing and t-tests. These concepts will be taught with heavy emphasis on statistical computing packages. Students will be expected to have a working knowledge of SAS, SPSS, JMP, and Minitab (satisfied through the pre-requisite of STAT 3010).
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3.00 Credits
In this course students use descriptive statistics and visual displays to describe data. They learn about some common population and sample distributions. They perform and analyze results of statistical inferences, including confidence intervals, correlation, linear regression, odds/risk ratios, and hypothesis testing (F and T-tests for regression, Chi-square for independence, 2 group and paired sample t-tests). Analyses are performed using MS-Excel. The student is required to select, analyze and interpret real life data for a project.
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3.00 Credits
Prerequisite: MATH 3332 or STAT 3120. This course is designed to build on the concepts and skills developed after taking STAT 3010 and STAT 3120. Concepts taught in this course include: Categorical Data Analysis, Correlation, Sampling, Analysis of Variance, Regression Analysis-method of least squares, general model building and data transformations. At least one of the following packages will be used: SAS, JMP, Minitab, SPSS.
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3.00 Credits
Prerequisite: STAT 3130. Methods for constructing and analyzing designed experiments are the focus of this course. The concepts of experimental unit, ran-domization, blocking, replication, error reduc-tion and treatment structure are introduced. The design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional factorial designs will be covered. Statistical software, including SPSS, Minitab and SAS will be utilized.
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3.00 Credits
Prerequisite: STAT 3130. Topics include simple linear regression, multiple regression models, generalized linear model, multicollinearity, qualitative predictor variables, model selection and validation, identifying outliers and influential observations, diagnostics for multicollinearity, and logistic regression and discriminant analysis.
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3.00 Credits
Data Mining is an information extraction activity whose goal is to discover hidden facts contained in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. The process includes data selection, cleaning, coding, using different statistical, pattern recognition and machine learning techniques, and reporting and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples of practical applications. The students will use recent SAS Enterprise Miner software.
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