Troy University Real Estate Worksheet

User Generated

eeeeenj

Business Finance

Troy University

Description

USE SAS

The word dox is the requirement and questions.

If you have properly completed your SAS project you will upload the following three items:

1. The DOCX file with the original assignment and rubric (all fields completed).

2. The XLSX file you downloaded with the addition of the tab with the Scatterplots on it and the Regression Output tab for the regression of Price with the three independent variables.

3. A PDF file you produce from SAS that shows the output of your final regression (with a higher R2 than we had with the original model).

Unformatted Attachment Preview

Name: _______________________________________________ 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. b. c. d. e. f. g. h. i. j. k. l. *Unit# *Type *Location *U/S/R Price Sq. Ft. Lot (Acres) Garage BRs Baths *Pool Age (An assigned database key) (H = House, C = Condo/Apartment) (1 through 10 – voting district where located) (Urban vs. Suburban vs. Rural location) (The price the house ended up selling for in 2017) (Heated/Cooled & Attached square footage) (Acreage of property) (Number of attached covered and/or enclosed parking positions) (Number of qualified bedrooms) (Number of bathrooms – no tub or shower indicated as .5) (No=No Access; HA=Shared Pool; AG=Above Ground; IG=In Ground) (Age of home in rounded year at end of 2017) At a high level, here are the steps you are going to perform: 1. 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. 2. Perform Regression Analysis within Excel to determine how well the prescribed Independent Variables explain changes in the Dependent Variable. 3. Upload the Real Estate dataset into SAS Studio. 4. Perform a series of Regression Analyses in SAS Studio to find a better set of explanatory variables. 5. 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. 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 Moderate Strong Moderate Strong Moderate Strong Moderate Strong Moderate Strong b. Between Price and Lot (Circle)? No relationship Weak c. Between Price and Garage (Circle)? No relationship Weak d. Between Price and BRs (Circle)? No relationship Weak e. Between Price and Baths (Circle)? No relationship f. Weak Between Price and Age (Circle)? No relationship Weak 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. 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. Which variable(s) would you remove (Circle)? Lot Size Garage BRs 7. Of the following variables in the spreadsheet, which variable 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 variable you chose in step 7. Print your 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. Provide the following from the SAS Model: a. Coefficient of Determination (R-squared ). ________________________ b. Y-Intercept for the Regression Model ________________________ c. Slope value for each of your Independent Variables. i. Var_______________________ ________________________ ii. Var_______________________ ________________________ iii. Var_______________________ ________________________ iv. Var_______________________ ________________________ v. Var_______________________ ________________________ 10. Did your SAS model provide a stronger Coefficient of Determination (Circle)? Yes No Critical Thinking Question: 11. A large real estate company is trying to use similar data plus their own sales data 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 to use the same data as we used above in the SAS model? Why or why not? __________________________________________________________________________________ __________________________________________________________________________________ b. Recommend two data elements you think they probably have available to help them predict sales for each of their sales people. 1. ______________________________________________ 2. ______________________________________________ GRADING RUBRIC Overall Score Possible = 100 Problem Area Did the student create the Excel tab for Scatterplots? Possible Points 2 Did the student create the correct scatterplots and move them to the new tab? 3 Did the student make a selection for each type of relationship? 5 Did the student run Data Analysis on the Excel spreadsheet creating a new tab for the model output? 10 Did the student provide the correct model output values from the spreadsheet in the problem document? 10 Did the student answer Critical Thinking questions 5, 6 and 7? 20 Did the student run a regression model in SAS and provide a print out of the model output? 20 Did the student provide the correct model output values from SAS in the problem document and answer the decision problem (#10)? 10 Did the student complete all parts of the Critical Thinking problem #11? 20 Total Critical Thinking Points 100 Points Awarded Unit # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Type H H H H H H H H H C H H H H H H H H H C H H H H H H H H H C H H H H H H H H H C H H H H H H Location 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 U/S/R R U S S S U S R S U R U S S S U S R S U R U S S S U S R S U R U S S S U S R S U R U S S S U Price $ 54.000 $ 98.000 $ 125.700 $ 250.000 $ 411.500 $ 56.500 $ 289.500 $ 420.000 $ 199.800 $ 249.900 $ 77.000 $ 78.600 $ 199.800 $ 279.500 $ 842.000 $ 66.720 $ 311.450 $ 311.520 $ 187.500 $ 311.750 $ 98.000 $ 112.000 $ 146.850 $ 301.500 $ 690.000 $ 71.200 $ 275.000 $ 598.230 $ 176.500 $ 405.200 $ 68.521 $ 101.500 $ 117.650 $ 266.000 $ 601.500 $ 39.800 $ 401.500 $ 782.000 $ 201.500 $ 199.650 $ 119.500 $ 88.420 $ 188.500 $ 231.100 $ 485.200 $ 48.999 Sq. Ft. 1100 1875 1350 2612 2190 1800 1605 2199 2120 900 1950 1420 2090 2770 3650 1600 2288 2000 1880 980 3011 2980 1850 3520 3300 1905 2850 3250 1900 1150 2015 2190 1750 2190 3450 1064 2540 4200 1980 850 1865 1750 1700 2045 2700 1550 Lot (Acres) 2 0,25 0,25 0,5 0,5 0,25 0,25 12 0,4 0 1 0,5 0,75 0,5 1 0,25 0,5 1,5 0,25 0 3 0,4 0,25 0,5 0,75 0,5 0,25 10 0,4 0 1,5 0,66 0,66 1 0,75 0,5 0,75 5 0,66 0 14 0,75 0,5 0,5 0,5 0,75 Garage 0 1 0 2 1 0 2 2 2 0 1 0 2 2 3 1 2 2 1 1 1 2 0 3 3 1 2 3 2 1 1 1 1 2 2 0 2 3 2 0 1 1 2 1 2 1 BRs 2 3 2 3 3 3 3 3 3 2 2 2 3 3 5 3 3 3 3 2 4 3 3 4 4 3 3 4 3 3 3 3 3 3 3 2 4 5 3 2 3 3 3 3 3 3 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 H H H C H H H H H H H H H C H H H H H H H H H C H H H H H H H H H C H H H H H H H H H C H H H 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 6 9 1 3 7 4 8 10 2 5 S R S U R U S S S U S R S U R U S S S U S R S U R U S S S U S R S U R U S S S U S R S U R U S $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 366.500 356.420 157.650 288.500 49.874 91.640 179.500 189.500 532.800 52.100 399.500 388.600 175.800 301.500 95.400 96.888 171.630 207.500 577.900 49.875 247.800 497.500 205.000 469.800 77.000 91.400 152.800 401.500 505.000 58.700 285.235 675.500 188.760 302.900 171.680 84.600 166.900 366.900 411.960 68.900 297.600 524.700 181.500 312.800 88.520 79.450 151.960 2390 2050 1830 1014 1450 1800 2015 1950 2888 2012 2450 3450 2200 1050 2220 1995 2100 2750 3120 1011 2120 3890 2100 1250 1090 1900 1040 3850 2950 1000 2850 2740 1850 900 2950 1640 1800 3200 2400 2200 3300 4350 1800 940 1750 1490 1500 0,5 9 0,25 0 0,5 0,5 0,75 0,5 0,5 0,4 0,5 4 0,4 0 8 0,5 1 0,75 0,75 0,25 0,5 22 0,66 0 2,5 0,4 0,25 1 0,5 0,4 0,5 75 0,25 0 11 0,75 0,8 0,75 0,5 0,5 0,5 11 0,4 0 4 0,5 0,5 2 2 1 1 0 2 1 2 2 1 2 2 2 1 2 1 2 2 2 0 2 3 2 2 0 1 1 2 2 1 1 2 1 1 2 1 2 3 2 2 2 2 2 1 1 0 1 4 3 2 2 2 3 3 3 4 3 3 3 3 2 3 2 3 3 4 2 2 4 4 3 3 2 2 4 3 2 3 3 2 2 3 2 3 4 3 3 4 4 3 2 2 3 3 94 95 96 97 98 99 100 101 H H H H H H C H 6 9 1 3 7 4 8 10 S S U S R S U R $ $ $ $ $ $ $ $ 302.900 489.650 64.995 400.500 711.000 172.450 345.900 81.400 2175 2550 850 2752 4540 1590 980 1275 1 0,5 0,25 1 18 0,5 0 2 2 2 0 2 2 1 1 1 3 3 2 3 5 2 3 2 Baths 1 2 1,5 2 2 1 2 2,5 2 2 2 2 2 2,5 5 1,5 2 2 2 1,5 2 2 2 2,5 3,5 1,5 2 2 2 2,5 1,5 2,5 1,5 2,5 3 1,5 2,5 2,5 2 2 2 2 2 2 2,5 2 Pool No No AG No No No HA No No HA No No IG HA HA No No IG IG HA AG No No IG HA AG No No No HA No IG No HA IG No No No HA HA No AG No No No No Age 27 26 82 11 17 21 6 72 15 4 12 16 22 9 4 28 11 21 9 5 35 4 11 3 9 37 5 2 3 0 38 16 22 8 6 31 9 4 8 6 17 21 15 8 15 29 2 2 2 2 1 2,5 2 2,5 2,5 2 2,5 2,5 2,5 2 2 1,5 2 2 3 2 2 3,5 2,5 2,5 1 2 2 3 3 2 2,5 2 2 2 2 2 2 2,5 2 2 2,5 3 2 2 2 1,5 2 No No No HA No No No No No No No No No HA IG No No IG HA No HA IG HA HA AG No IG HA IG AG IG IG No HA AG No No HA No No HA No No HA No No No 13 17 8 2 36 9 12 4 4 16 7 37 2 1 21 15 36 7 2 14 6 3 4 0 35 4 3 7 1 25 2 15 4 1 5 7 2 7 9 17 8 3 12 7 37 32 17 2 2,5 1,5 2,5 3 2 2 1,5 No No No HA No IG HA No 11 11 12 6 14 9 2 24 Name: _______________________________________________ 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. b. c. d. e. f. g. h. i. j. k. l. *Unit# *Type *Location *U/S/R Price Sq. Ft. Lot (Acres) Garage BRs Baths *Pool Age (An assigned database key) (H = House, C = Condo/Apartment) (1 through 10 – voting district where located) (Urban vs. Suburban vs. Rural location) (The price the house ended up selling for in 2017) (Heated/Cooled & Attached square footage) (Acreage of property) (Number of attached covered and/or enclosed parking positions) (Number of qualified bedrooms) (Number of bathrooms – no tub or shower indicated as .5) (No=No Access; HA=Shared Pool; AG=Above Ground; IG=In Ground) (Age of home in rounded year at end of 2017) At a high level, here are the steps you are going to perform: 1. 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. 2. Perform Regression Analysis within Excel to determine how well the prescribed Independent Variables explain changes in the Dependent Variable. 3. Upload the Real Estate dataset into SAS Studio. 4. Perform a series of Regression Analyses in SAS Studio to find a better set of explanatory variables. 5. 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. 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 Moderate Strong Moderate Strong Moderate Strong Moderate Strong Moderate Strong b. Between Price and Lot (Circle)? No relationship Weak c. Between Price and Garage (Circle)? No relationship Weak d. Between Price and BRs (Circle)? No relationship Weak e. Between Price and Baths (Circle)? No relationship f. Weak Between Price and Age (Circle)? No relationship Weak 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. 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. Which variable(s) would you remove (Circle)? Lot Size Garage BRs 7. Of the following variables in the spreadsheet, which variable 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 variable you chose in step 7. Print your 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. Provide the following from the SAS Model: a. Coefficient of Determination (R-squared ). ________________________ b. Y-Intercept for the Regression Model ________________________ c. Slope value for each of your Independent Variables. i. Var_______________________ ________________________ ii. Var_______________________ ________________________ iii. Var_______________________ ________________________ iv. Var_______________________ ________________________ v. Var_______________________ ________________________ 10. Did your SAS model provide a stronger Coefficient of Determination (Circle)? Yes No Critical Thinking Question: 11. A large real estate company is trying to use similar data plus their own sales data 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 to use the same data as we used above in the SAS model? Why or why not? __________________________________________________________________________________ __________________________________________________________________________________ b. Recommend two data elements you think they probably have available to help them predict sales for each of their sales people. 1. ______________________________________________ 2. ______________________________________________ GRADING RUBRIC Overall Score Possible = 100 Problem Area Did the student create the Excel tab for Scatterplots? Possible Points 2 Did the student create the correct scatterplots and move them to the new tab? 3 Did the student make a selection for each type of relationship? 5 Did the student run Data Analysis on the Excel spreadsheet creating a new tab for the model output? 10 Did the student provide the correct model output values from the spreadsheet in the problem document? 10 Did the student answer Critical Thinking questions 5, 6 and 7? 20 Did the student run a regression model in SAS and provide a print out of the model output? 20 Did the student provide the correct model output values from SAS in the problem document and answer the decision problem (#10)? 10 Did the student complete all parts of the Critical Thinking problem #11? 20 Total Critical Thinking Points 100 Points Awarded
Purchase answer to see full attachment
Explanation & Answer:
NA
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

Explanation & Answer

Attached.

Name: _______________________________________________
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.
b.
c.
d.
e.
f.
g.
h.
i.
j.
k.
l.

*Unit#
*Type
*Location
*U/S/R
Price
Sq. Ft.
Lot (Acres)
Garage
BRs
Baths
*Pool
Age

(An assigned database key)
(H = House, C = Condo/Apartment)
(1 through 10 – voting district where located)
(Urban vs. Suburban vs. Rural location)
(The price the house ended up selling for in 2017)
(Heated/Cooled & Attached square footage)
(Acreage of property)
(Number of attached covered and/or enclosed parking positions)
(Number of qualified bedrooms)
(Number of bathrooms – no tub or shower indicated as .5)
(No=No Access; HA=Shared Pool; AG=Above Ground; IG=In Ground)
(Age of home in rounded year at end of 2017)

At a high level, here are the steps you are going to perform:
1. 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.
2. Perform Regression Analysis within Excel to determine how well the prescribed Independent
Variables explain changes in the Dependent Varia...


Anonymous
Just what I needed. Studypool is a lifesaver!

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Similar Content

Related Tags