Data for Exercises 7.11 and 7.36

Indy500

## Format

A data frame/tibble with 33 observations on four variables

driver

a character variable with values andretti, bachelart, boesel, brayton, c.guerrero, cheever, fabi, fernandez, ferran, fittipaldi, fox, goodyear, gordon, gugelmin, herta, james, johansson, jones, lazier, luyendyk, matsuda, matsushita, pruett, r.guerrero, rahal, ribeiro, salazar, sharp, sullivan, tracy, vasser, villeneuve, and zampedri

qualif

qualifying speed (in mph)

starts

number of Indianapolis 500 starts

group

a numeric vector where 1 indicates the driver has 4 or fewer Indianapolis 500 starts and a 2 for drivers with 5 or more Indianapolis 500 starts

## References

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

## Examples


stripchart(qualif ~ group, data = Indy500, method = "stack",
pch = 19, col = c("red", "blue"))

boxplot(qualif ~ group, data = Indy500)

t.test(qualif ~ group, data = Indy500)
#>
#> 	Welch Two Sample t-test
#>
#> data:  qualif by group
#> t = -1.9197, df = 12.033, p-value = 0.07892
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#>  -3.0111081  0.1899245
#> sample estimates:
#> mean in group 1 mean in group 2
#>        226.4538        227.8644
#>
if (FALSE) {
library(ggplot2)
ggplot2::ggplot(data = Indy500, aes(sample = qualif)) +
geom_qq() +
facet_grid(group ~ .) +
theme_bw()
}