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Logistic Regression

Member Training: Multinomial Logistic Regression

by TAF Support  Leave a Comment

Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
[Read more…] about Member Training: Multinomial Logistic Regression

Tagged With: categorical variable, logistic regression, Multinomial Logistic Regression

Related Posts

  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Member Training: Hierarchical Regressions
  • Member Training: Goodness of Fit Statistics
  • Member Training: Explaining Logistic Regression Results to Non-Researchers

When Linear Models Don’t Fit Your Data, Now What?

by Karen Grace-Martin  32 Comments

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.

Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.

[Read more…] about When Linear Models Don’t Fit Your Data, Now What?

Tagged With: binary variable, categorical variable, Censored, dependent variable, Discrete Counts, Multinomial, ordinal variable, Poisson Regression, Proportion, Proportional Odds Model, regression models, Truncated, Zero Inflated

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  • 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption
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  • When to Check Model Assumptions
  • Proportions as Dependent Variable in Regression–Which Type of Model?

Guidelines for writing up three types of odds ratios

by Karen Grace-Martin  Leave a Comment

Odds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care. [Read more…] about Guidelines for writing up three types of odds ratios

Tagged With: binary predictor, logistic regression, multicategory predictor, numerical predictor, odds ratios

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  • Logistic Regression Analysis: Understanding Odds and Probability
  • Confusing Statistical Term #8: Odds
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Logistic Regression Analysis: Understanding Odds and Probability

by Karen Grace-Martin  3 Comments

Updated 11/22/2021

Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome.

People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent.

How Odds and Probability Differ

They measure the same thing on different scales. Imagine how confusing it would be if people used degrees Celsius and degrees Fahrenheit interchangeably. “It’s going to be 35 degrees today” could really make you dress the wrong way.

In measuring the likelihood of any outcome, we need to know [Read more…] about Logistic Regression Analysis: Understanding Odds and Probability

Tagged With: logistic regression, odds, odds ratio, probability

Related Posts

  • Confusing Statistical Term #8: Odds
  • The Difference Between Relative Risk and Odds Ratios
  • Effect Size Statistics in Logistic Regression
  • How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels

Member Training: Goodness of Fit Statistics

by TAF Support 


What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?

[Read more…] about Member Training: Goodness of Fit Statistics

Tagged With: count models, goodness of fit, linear regression, logistic regression, mixed model, Structural Equation Modeling

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  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Member Training: Hierarchical Regressions
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Member Training: Explaining Logistic Regression Results to Non-Researchers

by TAF Support 

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?

[Read more…] about Member Training: Explaining Logistic Regression Results to Non-Researchers

Tagged With: categorical variable, graphing, interaction, logistic regression, numeric variable

Related Posts

  • Member Training: Multinomial Logistic Regression
  • When Linear Models Don’t Fit Your Data, Now What?
  • Member Training: Logistic Regression for Count and Proportion Data
  • Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation

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