Data for Exercise 7.43

Remedial

## Format

A data frame/tibble with 84 observations on two variables

gender

a character variable with values female and male

score

math placement score

## References

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

## Examples


boxplot(score ~ gender, data = Remedial,
col = c("purple", "blue"))

t.test(score ~ gender, data = Remedial, conf.level = 0.98)
#>
#> 	Welch Two Sample t-test
#>
#> data:  score by gender
#> t = 0.20997, df = 68.546, p-value = 0.8343
#> alternative hypothesis: true difference in means is not equal to 0
#> 98 percent confidence interval:
#>  -4.285617  5.114189
#> sample estimates:
#> mean in group female   mean in group male
#>             19.34286             18.92857
#>
t.test(score ~ gender, data = Remedial, conf.level = 0.98)\$conf
#> [1] -4.285617  5.114189
#> attr(,"conf.level")
#> [1] 0.98
wilcox.test(score ~ gender, data = Remedial,
conf.int = TRUE, conf.level = 0.98)
#> Warning: cannot compute exact p-value with ties
#> Warning: cannot compute exact confidence intervals with ties
#>
#> 	Wilcoxon rank sum test with continuity correction
#>
#> data:  score by gender
#> W = 855.5, p-value = 0.218
#> alternative hypothesis: true location shift is not equal to 0
#> 98 percent confidence interval:
#>  -1.000053  3.000039
#> sample estimates:
#> difference in location
#>               1.000093
#>