EXAMPLE 12 Calculating SSE, the sum of squares error
Solution
Table 7 shows the -values and residuals for the data in Table 6. The SSE is then found by squaring each residual and taking the sum. Thus,
We know that is the regression line, according to the least-squares criterion, so no other possible straight line would result in a smaller SSE.
Student | Time | Actual score |
Predicted score |
Residual |
(Residual)2 |
---|---|---|---|---|---|
1 | 1 | 9 | 9 | 0 | 0 |
2 | 1 | 10 | 9 | 1 | 1 |
3 | 2 | 11 | 11 | 0 | 0 |
4 | 3 | 12 | 13 | −1 | 1 |
5 | 3 | 13 | 13 | 0 | 0 |
6 | 4 | 14 | 15 | −1 | 1 |
7 | 5 | 19 | 17 | 2 | 4 |
8 | 6 | 17 | 19 | −2 | 4 |
9 | 7 | 21 | 21 | 0 | 0 |
10 | 8 | 24 | 23 | 1 | 1 |
NOW YOU CAN DO
Exercises 11a–22a.