Cereal and Classification Below is a representation of a dataset on household purchase behavior for a particular cereal brand for two consecutive months. Household Income ($K/yr) Size of Household (# of People) Zip Code (5-digit number) Month 1 purchase (1: Yes, 0: No) Month 2 purchase (1: Yes, 0: No) You plan to use this data to create a model that predicts household purchase behavior for the cereal brand. Unless otherwise stated, the following questions assume the use of Logistic Regression. 1. Suppose the resulting model has a value of 0.4 as the coefficient for the [Size of Household] variable. What does this mean? a. All else equal, every additional person in a household increases the odds of purchasing this cereal in the next month by 40% b. All else equal, every additional person in a household increases the frequency of purchasing this cereal by 40% c. All else equal, every additional person in a household increases the total spend on this cereal by 40% d. None of the other choices is the correct interpretation. e. All else equal, every additional person in a household increases the probability of purchasing this cereal in the next month by 40%