Create Robust Linear Models Using Generalized Regression (303060)

Keywords: Generalized Regression, Linear Modeling

While not as widely known or used among professionals for creating models on quality and productivity data applications, recent advancement in analytic computing has made generalized regression models easier to set up, faster to run, and clearer to analyze. This means generalized regression can now be used to improve upon your traditional linear model method approaches (like standard least squares and stepwise) to create a more robust model, especially for 1) using generalized regression to handle complex data such as multicollinearity in your model inputs that traditional methods cannot handle, and 2) using generalized regression to get better screening for the most important factors in a model as compared to traditional methods. This session will also cover how to best set up generalized regression modeling to handle a wide range of data types and challenges (e.g., binomial, zero-inflated, etc.)