You are an investment consultant. An important client of yours wants to invest in real estate in a U.S. city that has been experiencing an uptick in growth. The client wants to purchase 10 real estate properties that will have the highest sales prices in the future. She pre-selected 25 potential houses that appear to be good deals. You job is to predict the sales prices of the 25 properties as close to reality as possible. Based on your prediction, you need to make the decision on which 10 properties (i.e., ranked highest in future sale prices) to invest.

To make predictions, the broker of the properties has given you the relevant information of the pre-selected 25 potential houses. The information contained in the "Prediction" data sheet.

In addition, you are given data collected on 125 houses sold in the city over the past 2 years. These data are contained in the "Sales" data sheet. The data collected on the houses include:

1. ID - the property ID, which is the unique IDs of the real estate properties

2. House Size - the size of the house in square feet

3. Lot Size- the size of the lot in acres

4. Rooms - the total number of rooms in the house

5. Bathrooms -the number of bathrooms in the house

6. Utilities - this describes the average monthly utility cost (in $)

7. Year Built - this describes the year when the house was constructed

8. Overall Condition - this is a rating of the overall condition of the house. The numeric scale of this rating is:

1 - Very Poor 2 - Poor 3 - Fair 4 - Average 5 - Good 6 - Excellent 7 - Very Excellent

9. HOA fee - this describes the monthly homeowner associate fee

10. Nearby School Rating - this is the rating of the property's school zone

11. Price (in $) - this is the sales price of the houses

Tasks

Your client would like you to help her interpret and eventually predict the house price. So here are your tasks.

1. Estimate a multiple regression equation of house price.

2. Based on the results, what do you think would predict house price? For example, does school rating influence the price of the house? Explain your answers (make sure to report the appropriate statistics that inform your conclusions).

3. The client wants to invest in 10 properties that will have the highest house price in the future. She pre-selected 25 potential houses that appear to be good businesses. You job is to predict the prices of the 25 houses. The data on the 25 potential houses are contained in the "Prediction" sheet.

4. Based on your prediction, you need to make the decision on which 10 houses (i.e., ranked highest in price) to invest in.

Note:

a. Report your soultions/answers to 1, 2, and 4 in the "Report" sheet.

b. For Task #3, fill in the predicted house prices in the "Prediction" sheet.

c. Generate a new data sheet (or as many as necessary) to show the relevant output/results of your analyses. You can achive by copying and pasting the StatTools outputs. Highlight relevant results and add comments, where needed. Please know that only the relevant ouputs should be included.