## Course Outline

• segmentGetting Started (Don't Skip This Part)
• segmentStatistics and Data Science: A Modeling Approach
• segmentPART I: EXPLORING VARIATION
• segmentChapter 1 - Welcome to Statistics: A Modeling Approach
• segmentChapter 2 - Understanding Data
• segmentChapter 3 - Examining Distributions
• segmentChapter 4 - Explaining Variation
• segmentPART II: MODELING VARIATION
• segmentChapter 5 - A Simple Model
• segmentChapter 6 - Quantifying Error
• segmentChapter 7 - Adding an Explanatory Variable to the Model
• segmentChapter 8 - Models with a Quantitative Explanatory Variable
• segmentPART III: EVALUATING MODELS
• Part III: Evaluating Models
• segmentChapter 9 - The Logic of Inference
• segmentChapter 10 - Model Comparison with F
• segmentChapter 11 - Parameter Estimation and Confidence Intervals
• segmentChapter 12 - What You Have Learned
• segmentFinishing Up (Don't Skip This Part!)
• segmentResources

### list High School / Advanced Statistics and Data Science I (ABC)

Book
• High School / Advanced Statistics and Data Science I (ABC)
• High School / Statistics and Data Science I (AB)
• High School / Statistics and Data Science II (XCD)
• College / Statistics and Data Science (ABC)
• College / Advanced Statistics and Data Science (ABCD)

# Part III: Evaluating Models

You have learned a lot so far. You now know something about the concept of distribution, and you have explored two types of distributions: the distribution of sample data, and the distribution of the population or Data Generating Process (DGP). You have learned how to specify and fit statistical models to data; how to quantify the fit of a model, and compare the fit of two models; and how to use models to make predictions and further sharpen your understanding of the DGP.

Although these models fit our data as well as possible, minimizing error as much as possible, there is still one problem: we don’t really know how good they are as models of the Data Generating Process. We know how good they are as models of our data, but not as models of the population from which our data came.

In Part III of the course we take up this issue. We start by more fully explaining the problem, and then take you through the solutions that statisticians use to evaluate models and quantify the error around our parameter estimates.

All of these solutions depend on the third distribution of the Distribution Triad, the sampling distribution (or as we will also refer to it, the distribution of estimates). Sampling distributions will be our focus as we enter the next part of the course. While the population distribution is hidden but real, this new kind of distribution is imaginary! Sampling distributions require you to think, “What if…?!” Let’s dive in!