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PMBA 6330 - Applied Data Analytics

Description
Data analytics introduces students to methods of data collection, storage, organization, and analysis. The course begins with an overview of descriptive statistics, graphical methods, probability, hypothesis testing, and modeling using linear regression analysis. Exploratory and confirmatory data analysis will be used to examine model specification issues such as dealing with measurement error, handling omitted variable bias, and determining the correct functional form. Students will then learn how to solve problems associated with the violation of the assumptions of linear regression including heteroskedasticity, multicollinearity, and autocorrelation. Finally, an introduction to maximum likelihood estimation for nonlinear, categorical, and limited dependent variable models will be provided. A portion of every class will be dedicated to learning how to use SAS in a lab-like setting to write programs to structure, estimate, and interpret statistical models.
Credits
3
Recent Professors
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Recent Semesters
Spring 2021, Spring 2020, Fall 2019, Spring 2019
Offered
Th, Tu
Avg. Class Size
25
Avg. Sections
2