Sales of Riding Mowers. 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 1000ft^2 ). The marketing expert looked at a random sample of 24 households. Use all the data to fit a logistic regression of ownership on the two predictors. Sales of Riding Mowers. 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 1000ft2 ). The marketing expert looked at a random sample of 24 households, given in the file RidingMowers.xls. Use all the data to fit a logistic regression of ownership on the two predictors.

Data is provided in the following link: https://docs.google.com/spreadsheets/d/14Qgz9we969fIiTTHTGgqZbYP6KaTg2--40V1bghm5_o/edit?usp=sharing

a. What percentage of households in the study were owners of a riding mower?

b. Create a scatterplot of Income versus Lot Size using color or symbol to differentiate owners from nonowners. From the scatterplot, which class seems to have the higher average income, owners or nonowners?

c. Among nonowners, what is the percentage of households classified correctly?

d. To increase the percentage of correctly classified nonowners, should the cutoff probability be increased or decreased?

e. What are the odds that a household with a $60K income and a lot size of 20,000ft^2 is an owner?

f. What is the classification of a household with a $60K income and a lot size of 20,000ft^2 ?