MN4110 Multivariate Manpower Data Analysis I

An introduction to multivariate data analysis. This section will focus on the tools necessary to perform data analysis. The primary goal of this course is to introduce multiple linear regression models. The second goal involves making correct inferences and interpretations of the findings. Special topics include hypothesis testing, model specification issues, multicollinearity, dummy variables, and research methodology.

Prerequisite

MN3041 or permission of instructor

Lecture Hours

4

Lab Hours

1

Course Learning Outcomes

By the end of this course, you should be able to: 

·       Assess threats to causality. 

·       Specify and estimate multiple regression models and interpret model coefficients, including natural logs, quadratics, and interaction terms. 

·       Explain partial effects. 

·       Evaluate model fit. 

·       Analyze policy questions and intergroup differences using binary data. 

·       Formulate and test hypotheses about population parameters. 

·       Differentiate between “causal analysis” and “machine learning”/“prediction.” 

·       Explain how linear regression coefficients are estimated.