# data[].attributes.body
After viewing the five different graphs of our data, we were able to find new information and observations. Our mean, or the average number of units sold, ended up being 8.75 million units. Our median, or middle number when viewing the data from least to greatest, was 5.69 million units. The five number summary of the data tells us the minimum (.89 million units), first quartile (2.57 million units), the median, the third quartile (10.81 million units) and the maximum (40.42 million units) of our data set. We chose this data set because most of the people in our group have a fondness for the overall wearing, kart racing and adventurous plummer known as Mario, and found that the sales of his various game titles would result in an interesting data set.

When looking at the box plot, it is easy to find the outliers of this particular data set. We had multiple outliers, as many of the Mario games vary greatly in terms of total units sold. It is skewed to the left, as no game has been able to beat the behemoth number that the original Super Mario Bros. game has sold. This is the cause of the steep decline of sales visible on many of the graphs, particular our bar and line graphs. This can be proven further when viewing our standard deviation, which comes to 9.01 million units, which is larger than the average units sold per game!

There were a couple last details we configured in regards to our data. The IQR ended up being 8.24 million after subtracting the first quartile from the third. There wasn't exactly a definite mode. However, it seemed that around 2 million copies is what the average minor entry Mario game will sell. This is honestly what we expected as we are both big Nintendo fans that realize that only the huge main entry games in the franchise will sell crazy large amounts of copies, but it's not like a Mario game to go below a million sold.

Of the four graphs we created, it was easy to compare and contrast them. Not one of them had any particular feature that was absent in another. They all just had different looks to them. The pie graph was convoluted with all of its colors, and the box plot is still a concept we have not fully grasped yet. Between the line and the bar graph, the bar graph takes the cake as it has always been more appealing to the eye. As much of a bore as working with graphs can be, it is always a little more tolerable to work with the data from everyone's favorite Italian plumber and his adventures.

# data[].attributes.body
Minimum: 0.89 million

1st Quartile: 2.57 million

Median: 5.69 million

3rd Quartile: 10.81 million

Maximum: 40.24 million

# data[].attributes.body
9.01 million units

# data[].attributes.body
8.75 million units