Course Outline
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segmentGetting Started (Don't Skip This Part)
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segmentStatistics and Data Science II
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segmentPART I: EXPLORING AND MODELING VARIATION
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segmentChapter 1 - Exploring Data with R
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segmentChapter 2 - From Exploring to Modeling Variation
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segmentChapter 3 - Modeling Relationships in Data
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segmentPART II: COMPARING MODELS TO MAKE INFERENCES
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segmentChapter 4 - The Logic of Inference
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segmentChapter 5 - Model Comparison with F
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segmentChapter 6 - Parameter Estimation and Confidence Intervals
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segmentPART III: MULTIVARIATE MODELS
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Part III: Multivariate Models
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segmentChapter 7 - Introduction to Multivariate Models
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segmentChapter 8 - Multivariate Model Comparisons
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segmentFinishing Up (Don't Skip This Part!)
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segmentResources
list High School / Statistics and Data Science II (XCD)
Part III: Multivariate Models
Up to now we have limited ourselves to models with just one explanatory (or predictor) variable (we will call these single-predictor models). In this section of the book we introduce multivariate models, i.e. models with more than one predictor variable.
We will start by building a model with one categorical predictor and one quantitative predictor. We will compare this multivariate model against the empty model and against the component single-predictor models.
Once you understand this model, you will be able to extend what you know to a whole host of models, including ones with more than two variables, ones with multiple categorical predictors, and ones with multiple quantitative predictors. As you will see, all of these models are just examples of the General Linear Model.