Application and validation of linear and logistic regression in machine learning; regression visualization; identification of outliers; identification of model shortcomings; missing value imputation; data transformations; making valid inferences and drawing business conclusions to recommend business actions on basis of fitted models. Prerequisites: Enrollment in Masters of Science in Analytics Credits 1 to 4. 1 to 4 Lecture Hours.
Application and validation of linear and logistic regression in machine learning; regression visualization; identification of outliers; identification of model shortcomings; missing value imputation; data transformations; making valid inferences and drawing business conclusions to recommend business actions on basis of fitted models. Prerequisites: Enrollment in Masters of Science in Analytics Credits 1 to 4. 1 to 4 Lecture Hours.
