You are a real estate broker and your clients always ask about utility costs when they are considering a new home for purchase. You decide to use your analytical skills to build a model that will enable you to predict the amount of the electric bill for a particular location based on a number of different factors. You have collected data and summarized it in a spreadsheet. In addition to the amount of the monthly electric bill, you have information on temperature, the number of days when the heat or air conditioner might need to run, whether or not a new meter or heat pump has been installed, and total energy consumption.

Dataset:

https://docs.google.com/spreadsheets/d/1vanPCoD3Vo1DNAbzBtjR48IdTjhUOaQc/edit?usp=sharing&ouid=110026528864652775525&rtpof=true&sd=true

Question

After several iterations, you arrive at Model which includes the predictors: TEMP, SIZE, METER, PUMP1, PUMP2. The amortized monthly cost of installing a new heat pump 2 is $40 / month. Does model indicate that it would be cost effective to install the new heat pump 2? Which of the following best describes the appropriate conclusion based on the model?

Select one:

a. No. After controlling for other statistically significant factors, it makes sense to purchase the new heat pump. b. Don't know. This model gives us no information about this decision.

c. Yes. Even after controlling for other statistically significant factors, it makes sense to purchase the new heat pump.