A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $1000s) and Lot Size (in 1000 ft2). A marketing expert at the company undertook a random sample of 24 households (12 owners and 12 nonowners) in the city. The data are in the file RidingMowers. xls. Using RapidMiner, create a scatterplot of Lot Size vs. Income, color coded by the outcome variable ownership. Based on the scatterplot, how would you classify a new household with $70,000 income and lot size 18,000 ft2? Explain your choice.
RidingMowers. xls:
Income Lot_Size Ownership
60 18.4 owner
85.5 16.8 owner
64.8 21.6 owner
61.5 20.8 owner
87 23.6 owner
110.1 19.2 owner
108 17.6 owner
82.8 22.4 owner
69 20 owner
93 20.8 owner
51 22 owner
81 20 owner
75 19.6 non-owner
52.8 20.8 non-owner
64.8 17.2 non-owner
43.2 20.4 non-owner
84 17.6 non-owner
49.2 17.6 non-owner
59.4 16 non-owner
66 18.4 non-owner
47.4 16.4 non-owner
33 18.8 non-owner
51 14 non-owner
63 14.8 non-owner