Data for Exercise 10.53

Nascar

## Format

A data frame/tibble with 36 observations on six variables

time

duration of pit stop (in seconds)

team

a numeric vector representing team 1, 2, or 3

ranks

a numeric vector ranking each pit stop in order of speed

## References

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

## Examples


boxplot(time ~ team, data = Nascar, col = rainbow(3))

model <- lm(time ~ factor(team), data = Nascar)
summary(model)
#>
#> Call:
#> lm(formula = time ~ factor(team), data = Nascar)
#>
#> Residuals:
#>     Min      1Q  Median      3Q     Max
#> -13.000  -3.375   0.125   3.938  12.000
#>
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)
#> (Intercept)     23.000      1.834  12.544 4.18e-14 ***
#> factor(team)2    3.500      2.593   1.350    0.186
#> factor(team)3   11.750      2.593   4.531 7.28e-05 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 6.351 on 33 degrees of freedom
#> Multiple R-squared:  0.3962,	Adjusted R-squared:  0.3596
#> F-statistic: 10.83 on 2 and 33 DF,  p-value: 0.0002426
#>
anova(model)
#> Analysis of Variance Table
#>
#> Response: time
#>              Df Sum Sq Mean Sq F value    Pr(>F)
#> factor(team)  2  873.5  436.75  10.826 0.0002426 ***
#> Residuals    33 1331.2   40.34
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
rm(model)