= Solution
The examination used separately emailed files. For this question a public https://github.com/friendly/Guerry[Guerry data archive] and its https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Guerry.csv[CSV mirror] permit a documented reconstruction: retain the 85 records with a recorded region and select the specified columns. All six printed records match exactly, and each of the five regions has 17 departments. The numerical results below refer to that reconstruction; the remaining rows cannot be checked against the unavailable emailed file.
Treat department identifiers as labels and region as a <categorical variable>. The following summaries show the scale and spread of the quantitative variables:
|| Variable
|| Mean
|| Standard deviation
|| Median
|| Interquartile range
|| Range
| CrimePers
| 19960.94
| 7299.24
| 18785
| 14790–26221
| 5883–37014
| CrimeProp
| 7881.34
| 3048.62
| 7624
| 5990–9190
| 1368–20235
| Literacy
| 39.14
| 17.43
| 38
| 25–52
| 12–74
| Donations
| 6723.32
| 4863.24
| 4964
| 3446–9242
| 1246–27830
| Wealth rank
| 43.58
| 25.11
| 44
| 22–65
| 1–86
A larger population-per-crime value means a lower crime rate, and a smaller wealth rank means greater recorded wealth. Donations are markedly right-skewed, as the large gap between mean and median and the upper tail indicate; rank is an ordinal measure rather than a monetary difference. For the ratio analysed next, the median is $2.683$, the interquartile interval is $[1.829,3.515]$, and the range is $[0.643,10.194]$.
Regional median ratios are $2.763$ in C, $2.502$ in E, $3.979$ in N, $1.373$ in S, and $2.937$ in W. The regional <box plots> and wealth-versus-log-ratio <scatter plot> in the figure expose this geographical contrast and an inverse association with wealth rank. \b[North has the highest typical property-to-person crime ratio and South the lowest in the reconstructed data.] These summaries are descriptive departmental comparisons, not causal or individual-level conclusions.
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