Excel
Sue8
CriticalThinkingExercise-RealEstate-NEW_2019.docxName: _______________________________________________
SAS® Forecasting Project for Critical Thinking
This project utilizes the “Real Estate – Base” database. The purpose is twofold:
· Build critical thinking skills needed to structure data analysis appropriately for effective decision making.
· Analyze available data practically and skillfully in order to build an explanatory regression model.
The Real Estate - Base database includes the following variables for 101 homes (* NOTE: These variables are shown as qualitative variables within the database):
a. *Unit# (An assigned database key)
b. *Type (H = House, C = Condo/Apartment)
c. *Location (1 through 10 – voting district where located)
d. *U/S/R (Urban vs. Suburban vs. Rural location)
e. Price (The price the house ended up selling for in 2017)
f. Sq. Ft. (Heated/Cooled & Attached square footage)
g. Lot (Acres) (Acreage of property)
h. Garage (Number of attached covered and/or enclosed parking positions)
i. BRs (Number of qualified bedrooms)
j. Baths (Number of bathrooms – no tub or shower indicated as .5)
k. *Pool (No=No Access; HA=Shared Pool; AG=Above Ground; IG=In Ground)
l. Age (Age of home in rounded year at end of 2017)
At a high level, here are the steps you are going to perform:
A. Download the Excel spreadsheet with the Real Estate Data in it and create the requested Scatterplots. NOTE: It is important that the Dependent Variable (Price) is on the Y-axis and the Independent Variable is on the X-axis. The order of the two columns will dictate that.
B. Perform Regression Analysis within Excel to determine how well the prescribed Independent Variables explain changes in the Dependent Variable.
C. Upload the original Real Estate dataset into SAS Studio (before the changes in Step 2).
D. Perform a series of Regression Analyses in SAS Studio to find a better set of explanatory variables.
E. Answer a critical thinking exercise regarding forecasting and the data set we have.
Here are the steps in detail:
1. Create the following charts in Excel using the charting tools and the indicated variables in “Real Estate - Base.xlsx” (Remember, Price is your Dependent Variable)
a. Create a new tab in the spreadsheet called “Scatterplots”. After creating each Scatterplot on the original tab, move it to the Scatterplot tab you created. Note that moving columns around may affect your already completed scatterplots!
b. Create a Scatterplot using the variables Price and Sq. Ft.
c. Create a Scatterplot using the variables Price and Lot (Acres).
d. Create a Scatterplot using the variables Price and Garage.
e. Create a Scatterplot using the variables Price and BRs.
f. Create a Scatterplot using the variables Price and Baths.
g. Create a Scatterplot using the variables Price and Age.
2. What sort of relationship do you see between these variables based on the scatterplots?
a. Between Price and Sq. Ft. (Circle)?
No relationship Weak Moderate Strong
b. Between Price and Lot (Circle)?
No relationship Weak Moderate Strong
c. Between Price and Garage (Circle)?
No relationship Weak Moderate Strong
d. Between Price and BRs (Circle)?
No relationship Weak Moderate Strong
e. Between Price and Baths (Circle)?
No relationship Weak Moderate Strong
f. Between Price and Age (Circle)?
No relationship Weak Moderate Strong
3. In the Excel spreadsheet provided, using the Data Analysis Add-in, run a regression analysis with Price as the Dependent Variable and Lot, Garage and BRs as the Independent Variables and select to have Excel create a new tab called “Regression Model”. It is recommended that you run individual regressions with each variable alone to see how strong each R2 is for Step 2.
4. Provide the following from the “Excel Model”:
a. Coefficient of Determination (R-squared) ___________________
b. Y-Intercept for the Regression Model ___________________
c. Slope value for X1 (Lot) ___________________
d. Slope value for X2 (Garage) ___________________
e. Slope value for X3 (BRs) ___________________
5. Do you think we need all three current Independent variables in our Regression model to predict changes in Price (Circle)? Yes No
Explain: _________________________________________________________________________
_______________________________________________________________________________
_______________________________________________________________________________
6. If we don’t need all three, which variable(s) would you remove (Circle)?
Lot Size Garage BRs
7. Of the following variables in the spreadsheet, which variables would you select next to add to the model (i.e., you think it would create a stronger prediction of Price)?
Type Location U/S/R Sq. Ft. Baths Pool Age
8. Run a SAS Regression Model on the Real Estate – Base database using Price as the Dependent Variable (Y) and include the original Independent Variables (minus any you removed in step 6) and adding the variables you chose in step 7. Print your final model output and turn it in with the assignment. (NOTE: You may have to repeat this exercise until you find a combination of variables that gives you a higher R2).
9. Did your SAS model provide a stronger Coefficient of Determination (Circle)? Yes No
Critical Thinking Question:
10. A large real estate company is trying to build a model to forecast total sales for the coming year for each of their agents and they have pulled data from their Finance records. They are trying to assemble the best data to build a Regression model.
a. Would it make sense for them to simply use the same database as we used above in the SAS model? Why or why not?
__________________________________________________________________________________
__________________________________________________________________________________
b. Recommend three data elements you think they probably have available to help them predict sales for each of their sales people.
1. ______________________________________________
2. ______________________________________________
3. ______________________________________________
GRADING RUBRIC
Overall Score Possible = 100
