## 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
• 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

# 4.0 Explaining Variation

## Introduction to Explaining Variation

Examining distributions of single variables is always an important starting place. But as data analysts, our interests usually go beyond exploring patterns of variation in a single variable. We want to explain the variation. In this section we begin thinking about what it means to explain variation.

We develop the idea that explaining variation in the most general sense means accounting for variation in one variable by variation in another variable. Explaining variation helps us in three ways: it helps us understand what causes the variation in a variable; it helps us predict future observations; or, it helps us change the system we are studying to produce different outcomes.

We develop some informal and qualitative methods for representing and exploring relationships among variables, and give you some practice generating explanations for variation. (In the next section we will introduce more quantitative methods for explaining variation using the concept of statistical model.)