Horses and Orchards in Wisconsin

In this section of the exercise, data related to the number of horses and the orchard acres per county in Wisconsin were analyzed. The goal was to find where the best places to invest in these types of farms are. At first, some parameters were calculated statistically, and then some maps were created and spatially studied.

                                                         Table 1 - Statistic Analysis

	Horses	Orchard Acres
Mean	1666	133.5
Median	1495	39.5
Mode	0	0
Skewness	1.43	7.18
Kurtosis	3.18	56.45
Standard Deviation	1156.46	389.17

Firstly, a general inquiry was made with the data available, obtaining the results presented in the Table 2. In both samples it was noticed that the median value is smaller than the mean. However, the difference wasn't too big.  Also for both samples, the mode was 0, meaning that the most frequent quantity of horses or orchard acres was 0. That makes perfect sense, considering is easier that two or more counties just don’t have the type of farming than having exactly the same amount of it.

In the skewness and kurtosis parameters, it’s apparent the prominence of the orchard acres. Most counties have their amount of orchards close to the mean, resulting in a peaked curve (high positive kurtosis), but these values are in general lower than the mean, reason why the positive skewness is also high. For the horses, the values are also positive, but not as high as for the orchards. The skewness is small, so there’s more results lower than the mean, but there’s no big discrepancy. However, the kurtosis is relatively high, showing that most of the values are near the mean.

The standard deviation in both cases suggests a lack of variety in the values that are lower than the mean. That’s because the standard deviation is almost as high as the mean value in the horse sample and even higher than the mean in the orchard sample. It means that there’re almost no samples in the -2 Standard Deviation section, so the outliers must be more frequent in the positive area.

After that, the production of Wisconsin maps could illustrate better how these values are distributed spatially. Examining the Map 1 and Map 2, the counties with more horses are concentrated in the south-west portion of Wisconsin, especially in Clark, Monroe, Vernon, Grant and Dane counties.

All of these mentioned counties can be considered prominent in the amount of horses. However, by analyzing how far their values are from the mean in the Map 3, Dane county stands out as the one with the higher amount of horses. Generally, it’s noticed a strong pattern in which the north-east region is weak in this variable and the north-west has the higher concentration of horses. Therefore, the investment would be better applied in locations within this area.

With the orchard farming, an opposite pattern was noticed. Almost all the counties don’t have a relatively high amount of orchard acres. It means that this kind on farming is highly concentrated in a few counties, generally in the west region of Wisconsin (Map 4), but with an extreme prominence of Door County, located in the east. This county has 33.34% of all the orchard acres in Wisconsin (Map 5), it’s clearly an outlier and it would be a really good place to invest the money. However, it’s only one county in the east region, so the tendency in the west might be interesting as well.

The decision-making with this analysis was based in the idea that the higher the number of the farming type, the more successful the farming is in that location. However, to guarantee better results, it would be interesting to have other variables about each farming type as well. Especially dealing with investments, to have the data about the profit made by each kind of farming per county would result in a more accurate answer. However, the available data fitted well in the purpose of having a general idea of how this kind of farmings are distributed within Wisconsin.