Statistical Models in Data Science 3,

4 Credits

Least square principles in multiple regression and their interpretations; estimation, hypotheses testing, confidence and prediction intervals, modeling, regression diagnostic, multicollinearity, model selection, analysis of variance and covariance; logistic regression. Introduction to topics in time series analysis such as ARMA, ARCH, and GARCH models. Applications to natural sciences, finance and economics. Use of computer packages. Prerequisites: ( MATH 012 or MATH 231 or MATH 264 ) and ( MATH 043 or MATH 205 or MATH 241 or MATH 242 or STAT 342 ) Attribute/Distribution: Q