a. Correlation, Slope, and Yintercept
r = .535, b = 2.0, a = 2.0
b. The regression equation is y´ = a +bx . a =
yintercept, b = Slope, y´ =
predicted score for x.
Case 
X 
Y 
Y´ 
1 
1 
2 
2.0+2.0(1) = 4 
2 
1 
6 
2.0+2.0(1) = 4 
3 
2 
5 
2.0+2.0(2) = 6 
4 
2 
7 
2.0+2.0(2) = 6 
Sum 



Case 
X 
Y 
Y´ 


1 
1 
2 
4 
25 = 3 
(3)^{2} = 9 
2 
1 
6 
4 
65 = 1 
(1)^{2} = 1 
3 
2 
5 
6 
55 = 0 
(0)^{2} = 0 
4 
2 
7 
6 
75 = 2 
(2)^{2} = 4 
Sum 




Σ = 14 
Case 
X 
Y 
Y´ 
(Y  Y´) 
(Y  Y´)^{2}^{ } 
1 
1 
2 
4 
24 = 2 
(2)^{2} = 4 
2 
1 
6 
4 
64 = 2 
(2)^{2} = 4 
3 
2 
5 
6 
56 = 1 
(1)^{2} = 1 
4 
2 
7 
6 
76 = 1 
(1)^{2} = 1 
Sum 




Σ = 10 
h. The largest deviations are for Cases 1 and 2, and the size of the deviation is 2.The smallest deviations are for Cases 3 and 4, and the size of the deviation is 1.
i. See g above. Both values should be 10
j. SS Predicted Calculations
Case 
X 
Y 
Y´ 

^{ } 
1 
1 
2 
4 
45 = 1 
(1)^{2} = 1 
2 
1 
6 
4 
45 = 1 
(1)^{2} = 1 
3 
2 
5 
6 
65 = 1 
(1)^{2} = 1 
4 
2 
7 
6 
65 = 1 
(1)^{2} = 1 
Sum 




Σ = 4 
l. See j above