Unit 5: Regression

Text reading and review exercises:

Review chapters 10, 11, and 12 of FPP and do the following review exercises:
Chapter 10 [pages 176-178]: 2, 3, 4, 7, 8, 9, 10
Chapter 11 [pages 198-201]: 2, 3, 4, 6, 9, 10
Chapter 12 [pages 213-216]: 2, 4, 5, 6

Reading:

Possible essay questions:

Computer project:

Boy Scouts and Girl Scouts are taught that a way to measure temperature is by counting cricket chirps and applying a formula to find the temperature. The data in the following reference gives the number of cricket chirps in a 15-second period and the temperature in degrees Farenheit at that time: Data on cricket chirps vs. temperature.

Copy the data above into a spreadsheet program, with the numbers of chirps in a 15-second period in the left column and the temperatures in the right column.
Create a scatter plot for the data. Put Chirps per Period on the horizontal axis and Temperature on the vertical axis.
In a sentence or two, describe the association that exists between the two variables. What type of association (linear, curvilinear or none? positive or negative?) is present? How strong is the association? (Type this into your spreadsheet so that it will come out when you print.)
Have the spreadsheet compute the averages and standard deviations of each variable and the correlation coefficient between them. (See the link below for help in getting this data from Excel.) Create a third column that contains "Temperature" values on the SD-line for each Chirps value.
Have Excel compute the slope and y-intercept of the regression line for Temperature on Chirps per Period. Label these values appropriately.
Create a fourth column to hold a list of "predicted" Temperature values lying on the regression line.
Create a second scatter diagram of the data that includes plots of the SD-line and the regression line for Temperature vs Chirps per Period. Change the style for the data points that represent the lines to actual lines (right-click on the data point and turn on lines and turn off markers).
Which is steeper, the SD-line or the regression line? Does this make sense? Why?

Excel instructions for finding the SD-line and regression line can be found in the supplement Using Excel 2: Slope and Intercept of Regression Lines. Another source of information is a video outlining a similar project.