Business Analytics II

Based primarily on multiple regression techniques, Business Analytics 2 searches for new insights and understanding of organizational performance. Business Analytics 2 presents the logical process of conducting a regression project. Topic coverage includes multiple linear regression techniques for quantitative and categorical cross sectional data, logistic regression, diagnostic techniques for detection of violations of model assumptions with recommended corrective actions, and methods for model selection. Additional topics include dealing with missing data, data reduction techniques, decision tree analysis, neural networks, and data mining. Emphasis is on using computer software to estimate models and reporting on the output from computer software, rather than theoretical statistical properties. Students are encouraged to analyze data related to their job if possible.