***PLEASE MAKE SURE TO LOOK AT THE NUMBERS AND NOT TO COPY THE SOLUTION FROM OTHER RESOURCES, AS VALUES CAN BE DIFFERENT IN THE TABLE

20. In a recent online course these data were collected on absences and exam scores. Use the data for the calculations requested below.

Student	Absences	Exam Score
1	3	82
2	1	98
3	2	76
4	3	68
5	1	84
6	0	99
7	4	67
8	5	58
9	7	50
10	3	78

a. Look at the data. What direction of relationship do you expect to find?

b. Compute r. To do this show calculations for SSx, SSy, and SP. Is this correlation significant? (use the correlation table to test this - show the CV for r 2-tailed)

c. Describe the direction of the relationship between the variables in a sentence. How much of the variance in test scores is related to absences?

d. Compute the linear regression equation (calculate a and b and write the equation).

e. Use the regression equationto predict the exam score of someone with 5 absences and someone with 1 absence. How far off do you expect your prediction to be?

[That is what the SE of estimate tells you folks! You don't have to calculate it! But if you wanted to calculate the SE of estimate for these data it is the square root of the MS residual - see 15.5 for analysis of regression. Or use JASP to find it. Or SSres = (1-r2) SSy divide by df =8 to get MSres and finally take sq root. ]