Course Outline

segmentGetting Started (Don't Skip This Part)

segmentIntroduction to Statistics: 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

5.9 Summarizing Where We Are

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

segmentChapter 9  Distributions of Estimates

segmentChapter 10  Confidence Intervals and Their Uses

segmentChapter 11  Model Comparison with the F Ratio

segmentChapter 12  What You Have Learned

segmentResources
list Introduction to Statistics: A Modeling Approach
Summarizing Where We Are
Up until this chapter, we used the DATA = MODEL + ERROR idea in a qualitative way. We built on this qualitative approach in this chapter to introduce our first statistical modelâ€”the simple (or empty) model, which we represented as DATA = MEAN + ERROR. As soon as we conceptualize a model as a number, then we can be more specific: we can be specific about which number we use for our model, and how to calculate it. And, we can be more specific about the meaning of error, defining it as the gap between our model prediction and an actual observed score (i.e., the residual).
But then we went and added a bunch of notation, which seems to complicate everything. In a sense, it does complicate everything, but in another sense, it simplifies everything, especially as we go forward. There are some key ideas we need to keep straight as we continue to work with models, and notation will help us do that.
Remember: our goal is to use our data distribution to construct a statistical model of the population distribution.