overfitting

Member Training: Model Building Approaches

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

Differences in Model Building Between Explanatory and Predictive Models

by Jeff Meyer 8 Comments

by Jeff Meyer, MPA, MBA

Suppose you are asked to create a model that will predict who will drop out of a program your organization offers. You decide to use a binary logistic regression because your outcome has two values: “0” for not dropping out and “1” for dropping out.

Most of us were trained in building models for the purpose of understanding and explaining the relationships between an outcome and a set of predictors. But model building works differently for purely predictive models. Where do we go from here? [Read more…] about Differences in Model Building Between Explanatory and Predictive Models

Member Training: The LASSO Regression Model

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The LASSO model (Least Absolute Shrinkage and Selection Operator) is a recent development that allows you to find a good fitting model in the regression context. It avoids many of the problems of overfitting that plague other model-building approaches.

In this month’s Statistically Speaking webinar, guest instructor Steve Simon, PhD, will explain what overfitting is — and why it’s a problem.

Then he’ll illustrate the geometry of the LASSO model in comparison to other regression approaches, ridge regression and stepwise variable selection.

Finally, he’ll show you how LASSO regression works with a real data set.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.