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

You decide to create Model 1 which is a "full model" including all of the predictor variables as a starting point Based on these model results match the number from the output that would best allow you to answereach of the following questions (responses have been rounded to three digits after the decimal): Note "Model 1" or the full model should include these predictors: (TEMP, HDD

, CDD, SIZE, METER, PUMP1, PUMP2, RIDERTOT)

Question

You decide to systematically eliminate some of the variables from the model. After several iterations, you arrive at Model 2 which includes the predictors: TEMP, SIZE, METER, PUMP1, PUMP2. Select all of the following that apply:

Select one or more:

a.The overall model in Model 1 was not significant while the overall model in Model 2 is.

b. All predictors in Model 2 are significant whereas some of the variables in Model 1 were not.

c. Model 2 seems to be a more parsimonious model.

d. Model 2 is better than model 1 because the adjusted r square increases.