Summary: This module provides an overview of Linear Regression and Correlation: Scatter Plots as a part of Collaborative Statistics collection (col10522) by Barbara Illowsky and Susan Dean.
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Before we take up the discussion of linear regression and correlation, we need to examine a
way to display the relation between two variables
From an article in the Wall Street Journal: In Europe and Asia,
mcommerce is becoming more popular. Mcommerce users have special mobile
phones that work like electronic wallets as well as provide phone and Internet services.
Users can do everything from paying for parking to buying a TV set or soda from a
machine to banking to checking sports scores on the Internet. In the next few years, will
there be a relationship between the year and the number of mcommerce users?
Construct a scatter plot. Let

A scatter plot shows the direction and strength of a relationship between the variables. A clear direction happens when there is either:
You can determine the strength of the relationship by looking at the scatter plot and seeing how close the points are to a line, a power function, an exponential function, or to some other type of function.
When you look at a scatterplot, you want to notice the overall pattern and any deviations from the pattern. The following scatterplot examples illustrate these concepts.



In this chapter, we are interested in scatter plots that show a linear pattern. Linear patterns
are quite common. The linear relationship is strong if the points are close to a straight line.
If we think that the points show a linear relationship, we would like to draw a line on the
scatter plot. This line can be calculated through a process called linear regression.
However, we only calculate a regression line if one of the variables helps to explain or
predict the other variable. If