Module 12: Linear Regression and Correlation

Scatter Plots

Barbara Illowsky & OpenStax et al.

Before we take up the discussion of linear regression and correlation, we need to examine a way to display the relation between two variables x and y. The most common and easiest way is a scatter plot. The following example illustrates a scatter plot.

Example

In Europe and Asia, m-commerce is popular. M-commerce 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. For the years 2000 through 2004, was there a relationship between the year and the number of m-commerce users? Construct a scatter plot. Let x = the year and let y = the number of m-commerce users, in millions.

(year) (# of users)
2000 0.5
2002 20.0
2003 33.0
2004 47.0

Table showing the number of m-commerce users (in millions) by year.

This is a scatter plot for the data provided. The x-axis represents the year and the y-axis represents the number of m-commerce users in millions. There are four points plotted, at (2000, 0.5), (2002, 20.0), (2003, 33.0), (2004, 47.0).

Scatter plot showing the number of m-commerce users (in millions) by year.

Creating a Scatter Plot

  1. Enter your X data into list L1 and your Y data into list L2.
  2. Press 2nd STATPLOT ENTER to use Plot 1. On the input screen for PLOT 1, highlight On and press ENTER. (Make sure the other plots are OFF.)
  3. For TYPE: highlight the very first icon, which is the scatter plot, and press ENTER.
  4. For Xlist:, enter L1 ENTER and for Ylist: L2 ENTER.
  5. For Mark: it does not matter which symbol you highlight, but the square is the easiest to see. Press ENTER.
  6. Make sure there are no other equations that could be plotted. Press Y = and clear any equations out.
  7. Press the ZOOM key and then the number 9 (for menu item “ZoomStat”) ; the calculator will fit the window to the data. You can press WINDOW to see the scaling of the axes.

try it

Amelia plays basketball for her high school. She wants to improve to play at the college level. She notices that the number of points she scores in a game goes up in response to the number of hours she practices her jump shot each week. She records the following data:

X (hours practicing jump shot) Y (points scored in a game)
5 15
7 22
9 28
10 31
11 33
12 36

Construct a scatter plot and state if what Amelia thinks appears to be true.

This is a scatter plot for the data provided. The x-axis is labeled in increments of 2 from 0 - 16. The y-axis is labeled in increments of 5 from 0 - 35.

Yes, Amelia’s assumption appears to be correct. The number of points Amelia scores per game goes up when she practices her jump shot more.


A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either: High values of one variable occurring with high values of the other variable or low values of one variable occurring with low values of the other variable. High values of one variable occurring with low values of the other variable.

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. For a linear relationship there is an exception. Consider a scatter plot where all the points fall on a horizontal line providing a “perfect fit.” The horizontal line would in fact show no relationship.

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.

The first graph is a scatter plot with 6 points plotted. The points form a pattern that moves upward to the right, almost in a straight line. The second graph is a scatter plot with the same 6 points as the first graph. A 7th point is plotted in the top left corner of the quadrant. It falls outside the general pattern set by the other 6 points.The first graph is a scatter plot with 6 points plotted. The points form a pattern that moves downward to the right, almost in a straight line. The second graph is a scatter plot of 8 points. These points form a general downward pattern, but the point do not align in a tight pattern.The first graph is a scatter plot of 7 points in an exponential pattern. The pattern of the points begins along the x-axis and curves steeply upward to the right side of the quadrant. The second graph shows a scatter plot with many points scattered everywhere, exhibiting no pattern.

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, except in the case of a horizontal line where there is no relationship. 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 x is the independent variable and y the dependent variable, then we can use a regression line to predicty for a given value of x

Concept Review

Scatter plots are particularly helpful graphs when we want to see if there is a linear relationship among data points. They indicate both the direction of the relationship between the x variables and the y variables, and the strength of the relationship. We calculate the strength of the relationship between an independent variable and a dependent variable using linear regression.

License

Icon for the Creative Commons Attribution 4.0 International License

Adapted By Darlene Young Inroductory Statistics by Barbara Illowsky & OpenStax et al. is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

Share This Book