Es folgt eine analyse der Daten.
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
Call:
lm(formula = y1 ~ x1, data = anscombe)
Residuals:
Min 1Q Median 3Q Max
-1.9213 -0.4558 -0.0414 0.7094 1.8388
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.000 1.125 2.67 0.0257 *
x1 0.500 0.118 4.24 0.0022 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.24 on 9 degrees of freedom
Multiple R-squared: 0.667, Adjusted R-squared: 0.629
F-statistic: 18 on 1 and 9 DF, p-value: 0.00217
Das Intercept beträgt 1.99.