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.

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

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.

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

Throughout the session you will be prompted to view short video segments. In addition to these excerpts, you may choose to watch the full-length video of this session.

Previously Introduced:

New in This Session:

mean
standard deviation

association
bivariate analysis
contingency table
co-variation
least squares line
line of best fit
quadrants
scatter plot
sum of squared errors

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