Compute the least squares regression line for the data in Exercise 4 of Chapter 10, Section 2 “The Linear Correlation Coefficient”. data: x 1 2 4 7 9 y 5 5 6 - 3 0
Solution :
X | Y | XY | X^2 | Y^2 |
1 | 5 | 5 | 1 | 25 |
2 | 5 | 10 | 4 | 25 |
4 | 6 | 24 | 16 | 36 |
7 | -3 | -21 | 49 | 9 |
9 | 0 | 0 | 81 | 0 |
n | 5 |
sum(XY) | 18.00 |
sum(X) | 23.00 |
sum(Y) | 13.00 |
sum(X^2) | 151.00 |
sum(Y^2) | 95.00 |
Numerator | -209.00 |
Denominator | 262.98 |
r | -0.7948 |
r square | 0.6316 |
Xbar(mean) | 4.6000 |
Ybar(mean) | 2.6000 |
SD(X) | 3.0067 |
SD(Y) | 3.4986 |
b | -0.9248 |
a | 6.8540 |
the least squares regression line for the data is ,
= 6.8540 - 0.9248 X
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