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
-
Part 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
-
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)
Book
Part II: Modeling Variation
In this section of the course we develop the concept of statistical model. We start with the simplest model, sometimes called the “empty model.” From there we move to more complex models.
We create statistical models in order to:
Explain variation in an outcome variable using one or more explanatory variables, and to better understand the Data Generating Process;
Predict the values of future observations, or samples;
Guide changes we can make to improve the outcomes of the system we are studying.