Categorical predictors (comparing means)
In this lecture we look at how to incorporate categorical predictors (group membership) into a linear model to compare the means of those categories/groups. We look at using dummy/indicator coding to compare means of two groups, then extend the example to look at 3 groups. We see that the parameter estimates (b) represent differences between group means. We also see that the overall model fit is assessed with the F-statistic, which is calculated in the same way as for models with continuous predictors. Learn R alongside these lectures with the discovr package (https://www.discovr.rocks/discovr/) Suggested soundtrack: Helloween: Halloween ( • HELLOWEEN - Halloween (Official Live Video) )

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Contrast coding

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Comparing means adjusted for other predictors (analysis of covariance)

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Logistic regression (categorical outcomes)

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Bayesian Inference: Overview

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Lecture 01: The General Linear Model

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GLM: Model fit and multiple predictors

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Null Hypothesis Significance Testing

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The Strange Math That Predicts (Almost) Anything

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The standard error and confidence intervals

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Multilevel models

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Trump Preps for 80th Birthday, Threatens to Hit Iran, Knicks Historic Win & Elon Musk Trillionaire!?

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Gaussian Processes

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The Beast of Bias

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Hypothesis Testing in Statistics

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Something is jamming GPS over Europe. Here's what we found

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But what is a Laplace Transform?

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Factorial Designs

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Repeated measures (afex)

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The PENIS of Statistics

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