Course Outline
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segmentGetting Started (Don't Skip This Part)
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segmentStatistics and Data Science: A Modeling Approach
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segmentPART I: EXPLORING VARIATION
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segmentChapter 1 - Welcome to Statistics: A Modeling Approach
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segmentChapter 2 - Understanding Data
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segmentChapter 3 - Examining Distributions
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segmentChapter 4 - Explaining Variation
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segmentPART II: MODELING VARIATION
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segmentChapter 5 - A Simple Model
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segmentChapter 6 - Quantifying Error
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segmentChapter 7 - Adding an Explanatory Variable to the Model
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segmentChapter 8 - Models with a Quantitative Explanatory Variable
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segmentPART III: EVALUATING MODELS
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segmentChapter 9 - The Logic of Inference
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segmentChapter 10 - Model Comparison with F
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segmentChapter 11 - Parameter Estimation and Confidence Intervals
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segmentChapter 12 - What You Have Learned
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segmentFinishing Up (Don't Skip This Part!)
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segmentResources
list Statistics and Data Science (ABC)
1.5 Goals of This Course
Well, we didn’t even let you get through the introduction without doing some actual R coding! Doing and thinking—these are the main things you should be filling your time with as you go through this course. Doing without thinking would reduce you to just rote memorization of procedures. Thinking without doing would be awfully boring—you would miss the exciting part!
Our goals for this course are as follows:
First, to learn how to analyze data, using R. We want you to end up well on your way to being truly competent with data.
Second, to understand the core concepts of the domain of statistics—the ideas that will help you make sense of the analyses you produce.
Third, to prepare you to learn more about statistics in the future. Statistics is a big field. Knowing a little is still useful, but you should feel ready to keep learning after you finish this course.
So, let’s get started!