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.

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# Stage 4

## Exogenous and Endogenous Variables in Structural Equation Modeling

In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.

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## Member Training: Interrupted Time Series

## Member Training: Difference in Differences

The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the five.

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## Member Training: A Guide to Latent Variable Models

An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.

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## Member Training: Quantile Regression: Going Beyond the Mean

In your typical statistical work, chances are you have already used quantiles such as the median, 25th or 75th percentiles as descriptive statistics.

But did you know quantiles are also valuable in regression, where they can answer a broader set of research questions than standard linear regression?

In standard linear regression, the focus is on estimating the mean of a response variable given a set of predictor variables.

In quantile regression, we can go beyond the mean of the response variable. Instead we can understand how predictor variables predict (1) the entire distribution of the response variable or (2) one or more relevant features (e.g., center, spread, shape) of this distribution.

For example, quantile regression can help us understand not only how age predicts the mean or median income, but also how age predicts the 75th or 25th percentile of the income distribution.

Or we can see how the inter-quartile range — the width between the 75th and 25th percentile — is affected by age. Perhaps the range becomes wider as age increases, signaling that an increase in age is associated with an increase in income variability.

In this webinar, we will help you become familiar with the power and versatility of quantile regression by discussing topics such as:

- Quantiles – a brief review of their computation, interpretation and uses;
- Distinction between conditional and unconditional quantiles;
- Formulation and estimation of conditional quantile regression models;
- Interpretation of results produced by conditional quantile regression models;
- Graphical displays for visualizing the results of conditional quantile regression models;
- Inference and prediction for conditional quantile regression models;
- Software options for fitting quantile regression models.

Join us on this webinar to understand how quantile regression can be used to expand the scope of research questions you can address with your data.

**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.**

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