• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

predictive models

Member Training: A Predictive Modeling Primer: Regression and Beyond

by guest contributer

Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! [Read more…] about Member Training: A Predictive Modeling Primer: Regression and Beyond

Tagged With: bagging, boosting, Bootstrap, cross-validation, decision trees, discriminant analysis, K-Nearest Neighbors, lasso, linear regression, logistic regression, predictive models, random forests, Regression, Resampling Techniques, ridge regression, shrinkage methods, subset selection, supervised learning, tree-based methods

Related Posts

  • Member Training: Generalized Linear Models
  • Member Training: Resampling Techniques
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: Goodness of Fit Statistics

Descriptives Before Model Building

by Jeff Meyer Leave a Comment

One approach to model building is to use all predictors that make theoretical sense in the first model. For example, a first model for determining birth weight could include mother’s age, education, marital status, race, weight gain during pregnancy and gestation period.

The main effects of this model show that a mother’s education level and marital status are insignificant.
[Read more…] about Descriptives Before Model Building

Tagged With: Model Building, predictive models, significant

Related Posts

  • Differences in Model Building Between Explanatory and Predictive Models
  • Overfitting in Regression Models
  • Model Building Strategies: Step Up and Top Down
  • 7 Practical Guidelines for Accurate Statistical Model Building

Using Predicted Means to Understand Our Models

by Jeff Meyer

The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.

It’s understandable that this can happen.  We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.

[Read more…] about Using Predicted Means to Understand Our Models

Tagged With: best practices, predictive models, predictor variable

Related Posts

  • What’s in a Name? Moderation and Interaction, Independent and Predictor Variables
  • Confusing Statistical Terms #1: The Many Names of Independent Variables
  • Confusing Statistical Terms #11: Confounder
  • Should I Specify a Model Predictor as Categorical or Continuous?

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

Related Posts

  • Overfitting in Regression Models
  • What It Really Means to Remove an Interaction From a Model
  • Simplifying a Categorical Predictor in Regression Models
  • Descriptives Before Model Building

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Analyzing Pre-Post Data

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT