Data for Exercise 9.34

Windmill

## Format

A data frame/tibble with 25 observations on two variables

velocity

wind velocity (miles per hour)

output

power generated (DC volts)

## Source

Joglekar, et al. (1989), Lack of Fit Testing when Replicates Are Not Available, The American Statistician, 43,(3), 135-143.

## References

Kitchens, L. J. (2003) Basic Statistics and Data Analysis. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning.

## Examples


summary(lm(output ~ velocity, data = Windmill))
#>
#> Call:
#> lm(formula = output ~ velocity, data = Windmill)
#>
#> Residuals:
#>      Min       1Q   Median       3Q      Max
#> -0.59869 -0.14099  0.06059  0.17262  0.32184
#>
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  0.13088    0.12599   1.039     0.31
#> velocity     0.24115    0.01905  12.659 7.55e-12 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.2361 on 23 degrees of freedom
#> Multiple R-squared:  0.8745,	Adjusted R-squared:  0.869
#> F-statistic: 160.3 on 1 and 23 DF,  p-value: 7.546e-12
#>
anova(lm(output ~ velocity, data = Windmill))
#> Analysis of Variance Table
#>
#> Response: output
#>           Df Sum Sq Mean Sq F value    Pr(>F)
#> velocity   1 8.9296  8.9296  160.26 7.546e-12 ***
#> Residuals 23 1.2816  0.0557
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1