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overfitting

Member Training: Model Building Approaches

by TAF Support

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?

[Read more…] about Member Training: Model Building Approaches

Tagged With: centering, interaction, lasso, Missing Data, Model Building, Model Fit, Multicollinearity, overfitting, Research Question, sample size, specification error, statistical model, Stepwise

Related Posts

  • What Is Specification Error in Statistical Models?
  • Member Training: The LASSO Regression Model
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • December Member Training: Missing Data

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

Tagged With: explanatory models, Model Building, overfitting, predictive models, predictors, significance testing, Training Data, validation data

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  • What It Really Means to Take an Interaction Out of a Model
  • Simplifying a Categorical Predictor in Regression Models
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Member Training: The LASSO Regression Model

by guest Leave a Comment

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.

[Read more…] about Member Training: The LASSO Regression Model

Tagged With: lasso, Model Building, overfitting, regression models

Related Posts

  • Member Training: Model Building Approaches
  • Member Training: A Predictive Modeling Primer: Regression and Beyond
  • Differences in Model Building Between Explanatory and Predictive Models
  • Member Training: Working with Truncated and Censored Data

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This Month’s Statistically Speaking Live Training

  • February Member Training: Choosing the Best Statistical Analysis

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

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