`R/BSDA-package.R`

`Indy500.Rd`

Data for Exercises 7.11 and 7.36

`Indy500`

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

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

```
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()
}
```