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

5.10 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  The Logic of Inference

segmentChapter 10  Model Comparison with F

segmentChapter 11  Parameter Estimation and Confidence Intervals

segmentPART IV: MULTIVARIATE MODELS

segmentChapter 12  Introduction to Multivariate Models

segmentChapter 13  Multivariate Model Comparisons

segmentFinishing Up (Don't Skip This Part!)

segmentResources
list College / Advanced Statistics and Data Science (ABCD)
5.10 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 model—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.
Data  Population  

Model constructed based on data (estimated)  Model we are trying to estimate (unknown)  
Word equation  Person i’s thumb = sample mean + error  Person i’s thumb = population mean + error 
More specific statement; model is the mean 
\(Y_i=\bar{Y}+e_i\) • \(Y_i\) is person i’s thumb • \(\bar{Y}\) is the sample mean • \(e_i\) is the difference between person i’s thumb length and the sample mean 
\(Y_i=\mu+\epsilon_i\) • \(Y_i\) is person i’s thumb • \(\mu\) is the population mean (unknown) • \(\epsilon_i\) is the difference between person i’s thumb length and the population mean (unknown) 
Most general; can be used for any oneparameter model 
\(Y_i=b_0+e_i\) • Can be used to represent any oneparameter model, estimated from data, not just the mean 
\(Y_i=\beta_0+\epsilon_i\) • Can be used to represent any oneparameter model of the population, not just the mean 