Data for Exercise 6.100

`Marked`

A data frame/tibble with 65 observations on one variable

- percent
percentage of marked cars in 65 Florida police departments

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

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

```
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
#>
```