CHAPTER 3 Describing Relationships. 3.1 Scatterplots and Correlation. Scatterplots and Correlation. IDENTIFY explanatory and response variables in situations where one variable helps to explain or influences the other.
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CHAPTER 3Describing Relationships
Scatterplots and Correlation
Most statistical studies examine data on more than one variable. In many of these settings, the two variables play different roles.
A response variable measures an outcome of a study.
An explanatory variable may help explain or influence changes in a response variable.
Tim wants to know if there is a relationship between height and weight. Kelly wants to know if she can predict a student’s weight from his or her height. Information about height is easier to obtain than information about weight!
Problem: Identify the explanatory and response variables, if possible for Tim and Kelly.
Solution: Tim is just interested in the relationship between the two variables, so there is no clear explanatory or response variable. Kelly is treating a student’s height as the explanatory variable and the student’s weight as the response variable.
A scatterplot shows the relationship between two quantitative variables measured on the same individuals. The values of one variable appear on the horizontal axis, and the values of the other variable appear on the vertical axis. Each individual in the data appears as a point on the graph.
How to Make a Scatterplot
To describe a scatterplot, follow the basic strategy of data analysis from Chapters 1 and 2. Look for patterns and important departures from those patterns.
How to Examine a Scatterplot
As in any graph of data, look for the overall pattern and for striking departures from that pattern.
• You can describe the overall pattern of a scatterplot by the direction, form, and strength of the relationship.
• An important kind of departure is an outlier, an individual value that falls outside the overall pattern of the relationship.
Two variables have a positive association when above-average values of one tend to accompany above-average values of the other and when below-average values also tend to occur together.
Two variables have a negative association when above-average values of one tend to accompany below-average values of the other.
Describe the scatterplot.
There is a moderately strong, negative, curved relationship between the percent of students in a state who take the SAT and the mean SAT math score.
Further, there are two distinct clusters of states and two possible outliers that fall outside the overall pattern.
Direction: In general, it appears that teams that score more points per game have more wins and teams that score fewer points per game have fewer wins. We say that there is a positive associationbetween points per game and wins.
Form: There seems to be a linear pattern in the graph (that is, the overall pattern follows a straight line).
Strength: Because the points do not vary much from the linear pattern, the relationship is fairly strong. There do not appear to be any values that depart from the linear pattern, so there are no outliers.
Read pages 142-149
Problems #1,5,7,11,13 pages 159-160
Write a few sentences in your notebook to answer the following questions:
Describes the relationship between two categorical variables
Describes the relationship between a categorical and a quantitative variable
A scatterplot displays the strength, direction, and form of the relationship between two quantitative variables.
Linear relationships are important because a straight line is a simple pattern that is quite common. Unfortunately, our eyes are not good judges of how strong a linear relationship is.
The formula for r is a bit complex. It helps us to see what correlation is, but in practice, you should use your calculator or software to find r.
How to Calculate the Correlation r
Suppose that we have data on variables x and y for n individuals. The values for the first individual are x1 and y1, the values for the second individual are x2 and y2, and so on. The means and standard deviations of the two variables are x-bar and sx for the x-values and y-bar and sy for the y-values.
The correlation r between x and y is:
Calculate and interpret r
Each member of a small statistics class ran a 40-yard sprint and then did a long jump (with a running start). The table shows the sprint time (in seconds) and the long-jump distance (in inches)
Calculate and interpret r
Correlation tells us nothing about the form of the association. Be sure to check your scatterplot for form
A single point can have a large effect on the correlation.
Correlation is not a resistant measure
How correlation behaves is more important than the details of the formula. Here are some important facts about r.
Correlation makes no distinction between explanatory and response variables.
r does not change when we change the units of measurement of x, y, or both.
The correlation r itself has no unit of measurement.
Read pages 150-157
Problems #14-18, 21 pages 161-162
Following the debut of the new SAT Writing test in March 2005, Dr. Les Perelman from the Massachusetts Institute of Technology stirred controversy by reporting, “It appeared to me that regardless of what a student wrote, the longer the essay, the higher the score.” He went on to say, “I have never found a quantifiable predictor in 25 years of grading that was anywhere as strong as this one. If you just graded them based on length without ever reading them, you’d be right over 90% of the time.” The table below shows the data that Dr. Perelman used to draw his conclusions.
Carry out your own analysis of the data. Write a few sentences with numerical support to respond to Dr. Perelman’s claim. Be sure to include the r value and comment on the scatterplot for the data.