EDBA 731C Quantitative Research Methods III (2)

This course further develops competencies in using of regression analysis for social science research. We will practice using basic multivariate linear regression model using ordinary least squares (OLS), along with the statistical inference tools necessary for hypothesis testing. The course continues examining the consequences of violating the assumptions of the OLS model and how to correct for those problems. We demonstrate several techniques, including adjustments for heteroskedasticity and autocorrelation, and the instrumental variables technique. Students will learn how to utilize database systems (such as SQL and NoSQL), analytics software (such as R, Python, and SAS), and how to make trustworthy predictions using traditional statistics and machine learning methods. Topics include supervised (prediction and classification) and unsupervised (exploratory data analysis, principal components, cluster analysis) learning; prediction models including multiple linear regression, nonlinear regression, time series forecasting, artificial neural networks, regression trees, k-nearest neighbors; classification models including logit/probit models and classification trees. It also covers econometric methods in parallel with other statistical methods. Prerequisite(s): EDBA 731B Quantitative Research Methods II (2)

Grading Basis: Graded