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deviance

What Are Nested Models?

by Karen Grace-Martin Leave a Comment

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

  • All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
  • Linear Mixed Models
  • Generalized Linear Mixed Models
  • Parametric Survival Analysis models, like Weibull models
  • Structural Equation Models

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require [Read more…] about What Are Nested Models?

Tagged With: covariance parameters, deviance, fixed effect, likelihood ratio test, linear mixed model, linear model, Model Building, nested models

Related Posts

  • Mixed Models: Can you specify a predictor as both fixed and random?
  • Member Training: Hierarchical Regressions
  • Member Training: Types of Regression Models and When to Use Them
  • Covariance Matrices, Covariance Structures, and Bears, Oh My!

Member Training: The Multi-Faceted World of Residuals

by Karen Grace-Martin 1 Comment

Most analysts’ primary focus is to check the distributional assumptions with regards to residuals. They must be independent and identically distributed (i.i.d.) with a mean of zero and constant variance.

Residuals can also give us insight into the quality of our models.

In this webinar, we’ll review and compare what residuals are in linear regression, ANOVA, and generalized linear models. Jeff will cover:

  • Which residuals — standardized, studentized, Pearson, deviance, etc. — we use and why
  • How to determine if distributional assumptions have been met
  • How to use graphs to discover issues like non-linearity, omitted variables, and heteroskedasticity

Knowing how to piece this information together will improve your statistical modeling skills.


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 Multi-Faceted World of Residuals

Tagged With: ANOVA, deviance, generalized linear models, linear regression, Pearson Correlation, residuals, standardized, studentized

Related Posts

  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables
  • Same Statistical Models, Different (and Confusing) Output Terms
  • Member Training: The Anatomy of an ANOVA Table

Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output

by guest 3 Comments

by David Lillis, Ph.D.

In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set [Read more…] about Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output

Tagged With: AIC, Akaike Information Criterion, deviance, generalized linear models, GLM, Hosmer Lemeshow Goodness of Fit, logistic regression, R

Related Posts

  • Generalized Linear Models in R, Part 5: Graphs for Logistic Regression
  • Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation
  • Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities
  • Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression

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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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