Approved by: Graduate Council
Implementation Date: Spring 2016
Note: Deletions are strikethroughs. Insertions are underlined.
Catalog Copy
STAT 5110. Applied Regression Analysis (3). Cross-listed as DSBA 5110. Prerequisites: MATH 2164 and STAT 2122 or STAT3128 or equivalent, or permission of the department. This course provides theoretical and practical training in statistical modeling with particular emphasis on the application of linear regression and multivariate statistical analysis. The basic fundamentals and statistical inference techniques associated with regression models will be introduced. Students will also learn how to apply the statistical techniques to extract information from data generated in various application areas using statistical software. Topics generally covered include but not limited to linear regression, model adequacy checking and diagnostics, generalized linear regression, and multivariate statistical analysis. (Fall)
DSBA 5110. Applied Regression Analysis (3). Cross-listed as STAT 5110. Prerequisites: MATH 2164 and STAT 2122 or STAT3128 or equivalent, or permission of the department. This course provides theoretical and practical training in statistical modeling with particular emphasis on the application of linear regression and multivariate statistical analysis. The basic fundamentals and statistical inference techniques associated with regression models will be introduced. Students will also learn how to apply the statistical techniques to extract information from data generated in various application areas using statistical software. Topics generally covered include but not limited to linear regression, model adequacy checking and diagnostics, generalized linear regression, and multivariate statistical analysis. (Fall)
STAT 6115. Statistical Learning with Big Data (3). Cross-listed as DSBA 6115. Prerequisite: STAT 5110 or STAT 5123 or permission of the department. This course provides students a survey of major statistical learning concepts and methods for big data analysis, including both supervised and unsupervised learning such as resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, statistical graphics. Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces. (Spring)
DSBA 6115. Statistical Learning with Big Data (3). Cross-listed as STAT 6115. Prerequisite: STAT 5110 or STAT 5123 or permission of the department. This course provides students a survey of major statistical learning concepts and methods for big data analysis, including both supervised and unsupervised learning such as resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, statistical graphics. Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces. (Spring)