STAT 572 - Applied Regression Analysis for Business

3 Credit Hours

Matrix approach to multiple linear regression. Normal equations, interaction and confounding, use of dummy variables, model selection. Leverage, influence and collinearity. Autocorrelated errors. Logistic regression, maximum likelihood estimation, analysis of deviance, retrospective studies. Tree based models for discrete and continuous outcomes. Robust regression, and weighted least squares. Applications involving predictive modeling for credit risk and customer acquisition. Case studies from accounting, finance, and marketing.
(RE) Prerequisite(s): 571 and matrix algebra.

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STAT 572 - Applied Regression Analysis for Business