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logistic regression

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: Logistic Regression for Count and Proportion Data
  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Member Training: Types of Regression Models and When to Use Them
  • How to Combine Complicated Models with Tricky Effects

A Visual Description of Multicollinearity

by Karen Grace-Martin 2 Comments

Multicollinearity is one of those terms in statistics that is often defined in one of two ways:

1. Very mathematical terms that make no sense — I mean, what is a linear combination anyway?

2. Completely oversimplified in order to avoid the mathematical terms — it’s a high correlation, right?

So what is it really? In English?

[Read more…] about A Visual Description of Multicollinearity

Tagged With: confounding variable, correlations, linear combination, linear regression, logistic regression, Multicollinearity, predictor variable, Regression, regression coefficients, variance, Variance inflation factor

Related Posts

  • Eight Ways to Detect Multicollinearity
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case
  • Centering for Multicollinearity Between Main effects and Quadratic terms
  • Same Statistical Models, Different (and Confusing) Output Terms

Linear Regression for an Outcome Variable with Boundaries

by Karen Grace-Martin 4 Comments

The following statement might surprise you, but it’s true.

To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is:

  • Continuous
  • Unbounded
  • Measured on an interval or ratio scale

The normality assumption is about the errors in the model, which have the same distribution as Y|X. It’s absolutely possible to have a skewed distribution of Y and a normal distribution of errors because of the effect of X. [Read more…] about Linear Regression for an Outcome Variable with Boundaries

Tagged With: bounded, categorical variable, ceiling effect, floor effect, linear regression, logistic regression, unbounded

Related Posts

  • Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
  • Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
  • A Visual Description of Multicollinearity
  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience

Member Training: A Predictive Modeling Primer: Regression and Beyond

by guest

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: Explaining Logistic Regression Results to Non-Researchers

How to Decide Between Multinomial and Ordinal Logistic Regression Models

by Karen Grace-Martin 6 Comments

A great tool to have in your statistical tool belt is logistic regression.

It comes in many varieties and many of us are familiar with the variety for binary outcomes.

But multinomial and ordinal varieties of logistic regression are also incredibly useful and worth knowing.

They can be tricky to decide between in practice, however.  In some — but not all — situations you [Read more…] about How to Decide Between Multinomial and Ordinal Logistic Regression Models

Tagged With: link function, logistic regression, logit, Multinomial Logistic Regression, Ordinal Logistic Regression

Related Posts

  • Logistic Regression Models for Multinomial and Ordinal Variables
  • Link Functions and Errors in Logistic Regression
  • Proportions as Dependent Variable in Regression–Which Type of Model?
  • Member Training: Explaining Logistic Regression Results to Non-Researchers

Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 4 Comments

Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.

When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).

So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.

[Read more…] about Eight Ways to Detect Multicollinearity

Tagged With: Bivariate Statistics, Correlated Predictors, linear regression, logistic regression, Multicollinearity, p-value, predictor variable, regression models

Related Posts

  • A Visual Description of Multicollinearity
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Is Multicollinearity the Bogeyman?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

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