Data for Exercise 6.100

Marked

## Format

A data frame/tibble with 65 observations on one variable

percent

percentage of marked cars in 65 Florida police departments

## Source

Law Enforcement Management and Administrative Statistics, 1993, Bureau of Justice Statistics, NCJ-148825, September 1995, p. 147-148.

## References

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

## Examples


EDA(Marked$percent) #> [1] "Marked$percent"

#> Size (n)  Missing  Minimum   1st Qu     Mean   Median   TrMean   3rd Qu
#>   65.000    0.000   37.000   54.000   61.108   60.000   60.932   68.000
#>     Max.   Stdev.     Var.  SE Mean   I.Q.R.    Range Kurtosis Skewness
#>   92.000    9.980   99.598    1.238   14.000   55.000    0.273    0.443
#> SW p-val
#>    0.072
SIGN.test(Marked$percent, md = 60, alternative = "greater") #> #> One-sample Sign-Test #> #> data: Marked$percent
#> s = 32, p-value = 0.4495
#> alternative hypothesis: true median is greater than 60
#> 95 percent confidence interval:
#>  57.33704      Inf
#> sample estimates:
#> median of x
#>          60
#>
#> Achieved and Interpolated Confidence Intervals:
#>
#>                   Conf.Level L.E.pt U.E.pt
#> Lower Achieved CI     0.9320 58.000    Inf
#> Interpolated CI       0.9500 57.337    Inf
#> Upper Achieved CI     0.9592 57.000    Inf
#>
t.test(Marked$percent, mu = 60, alternative = "greater") #> #> One Sample t-test #> #> data: Marked$percent
#> t = 0.89485, df = 64, p-value = 0.1871
#> alternative hypothesis: true mean is greater than 60
#> 95 percent confidence interval:
#>  59.04171      Inf
#> sample estimates:
#> mean of x
#>  61.10769
#>