ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Generalized Regression – A Unified Framework for Linear Models

3 September 2018, 11:00 – 11:30

Abstract

Submitted by
Chris Gotwalt
Authors
Chris Gotwalt (Director of Statistical Research and Development, SAS Institute - JMP Division)
Abstract
Linear and generalized linear models are the central modeling tools of the applied statistician. Although there is a lot in common between, say, logistic regression, the analysis of designed experiments, and the application of modern variable selection methods like the Lasso, learning how to use these methods is made more complicated than it should.

This is because, even within the same software package, the terminology and user interface to these methods is often quite different. The Generalized Regression platform in JMP Pro, unlike previous linear modeling tools, provides a common framework for a vast array of models. It has been designed so that wherever the same concepts apply in different classes of models they have the same names and the output matches closely. Furthermore, with its Interactive Solution Path, one is able to instantly explore and evaluate the tradeoffs of different models.

In this session, we will illustrate the use of Generalized Regression on several types of models. We will give special attention to how it can be used to demonstrate visually to students the consequences of underfitting a model, the poor generalization performance of overfitting, as well as the immediate practical consequences of mishandling multicollinear data. We believe that this user interface can streamline the way that statistical modeling is taught in a way that makes the concepts much more clear to students.

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