# Bivariate Data and Analysis

## Analyze bivariate data and understand the concepts of association and co-variation between two quantitative variables. Explore scatter plots, the least squares line, and modeling linear relationships.

In This Session
Part A: Scatter Plots
Part B:
Contingency Tables
Part C: Modeling Linear Relationships
Part D: Fitting Lines to Data
Homework

In previous sessions, you provided answers to statistical problems by collecting and analyzing data on one variable. This kind of data analysis is known as univariate analysis. It is designed to draw out potential patterns in the variation in order to provide better answers to statistical questions. In your exploration of univariate analysis, you investigated several approaches to organizing data in graphs and tables, and you explored various numerical summary measures for describing characteristics of a distribution.

In this session, you will study statistical problems by collecting and analyzing data on two variables. This kind of data analysis, known as bivariate analysis, explores the concept of association between two variables. Association is based on how two variables simultaneously change together — the notion of co-variation.

Learning Objectives
The goal of this lesson is to understand the concepts of association and co-variation between two quantitative variables. In your investigation, you will do the following:
• Graph bivariate data in a scatter plot
• Divide the points in a scatter plot into four quadrants
• Summarize bivariate data in a contingency table
• Model linear relationships
• Explore the least squares line

### Key Terms

Previously Introduced:

New in This Session: