Data for Exercise 9.34
WindmillA data frame/tibble with 25 observations on two variables
wind velocity (miles per hour)
power generated (DC volts)
Joglekar, et al. (1989), Lack of Fit Testing when Replicates Are Not Available, The American Statistician, 43,(3), 135-143.
Kitchens, L. J. (2003) Basic Statistics and Data Analysis. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning.
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