QMS442: Multiple Regression for Business SPSS INDIVIDUAL PROJECT - Draft WINTER 2022 DUE: April 7, 2022, by 11:59 pm. MARKS: Total marks = 100 (or 20% of the final grade) PENALTY: There will be a...

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QMS442: Multiple Regression for Business
SPSS INDIVIDUAL PROJECT - Draft
WINTER 2022
DUE: April 7, 2022, by 11:59 pm.
MARKS: Total marks = 100 (or 20% of the final grade)    
PENALTY: There will be a 25-mark penalty (or 25% of the project mark) for every day after the due date (including weekends). As per the course outline, there are no extensions, including for AAS students.
Notes:
1. No handwritten reports will be considered for marking purposes.
2. Submit the project with a cover page indicating your name, Ryerson ID number and your data set file name. The project should include the report, SPSS data and output files, and original Excel data file.
3. Present your report in the form of a discussion to address the topics we have covered this year as per the course outline.
4. Write the body of your report in Word (and submit it in word.doc format, not pdf).
5. Number all pages of the body of your report.
6. Upload all your SPSS data files (sav and spv format for input and output respectively) and Excel file with your report as appendices online via D2LAssignment submissions.
7. Your report should include relevant tables and graphs in the body of the report which you have exported/copied from SPSS. Do not direct the reader to the appendices but include them where they are discussed.
8. You have a unique data set. It is your responsibility to use your unique data set. Failure to follow this will result in a zero mark, and an Academic Misconduct investigation.
9. Your report will be submitted to Turnitin, a plagiarism prevention and detection service, as per the course outline. Issues of plagiarism will be investigated for Academic Misconduct.
How the Project is graded
Your submission will be graded based upon the following factors: substance, presentation, accuracy, grammar, and clarity. A demonstration of effort is the required. Completeness to the elements of multiple regression discussed in this course is driving force of this assignment. Assignments will be compared to discern levels of effort and excellence.
Your Report
Your report is based on the information in your datafile, and the objective to predict sales prices using multiple regression analysis.
Your data file CONDO – set #.xlsx contains the following variables:
    PRICE100
    FLOOR
    DIST
    VIEW
    END
    FURNISH
    SOLD
Price: in hundreds of dollars
Floor: which floor the unit is on (1-8)
Distance from the elevator: in units (i.e. number of condo units away)
View: whether the unit has an ocean-view (1 = oceanview; 0 = not an ocean view)
End: whether the unit is an end unit (units ending in ‘11’ on each floor) with an obstructed view of the ocean (1 = obstructed view; 0 = not an obstructed view)
Furnished: whether the unit was sold “furnished” (1= furnished unit; 0 = unfurnished unit)
Sold: whether the unit was sold at Auction (“A”), or at the developer’s Set price (“F”)
A map of the layout of the condo building is provided in Case Study 5 in your textbook, page 458.
MODELS
Create seven (7) to nine (9) models of increasing complexity, to explain price based on the information you have. At a minimum you must include the following models:
Model 1: first order multiple regression
Model 2: second order multiple regression for the quantitative variables
Model 3: second order multiple regression, for the quantitative variables, and interaction with qualitative data.
Model 4: floor height is considered a dummy variable, and interaction with distance, view, at a minimum.
Model 5: At least one nested model (versus Model 3).
DISCUSS
Discuss your results for each model, considering the following:
· Do the data suit a regression model? Note especially any curvature in the data.
· Are the assumptions for regression met? Discuss this in detail.
· What is the equation for the model? Explain this equation. What does it suggest is important in determining price?
· Is the model significant? Are the coefficients? How do you know? (i.e., what is the hypothesis being tested?)
· Does the model make sense? Why?
Looking at the models you developed:
· Which of the nested models is prefe
ed? Why?
· Choose which model you think is best of all the models you developed. Why is this the prefe
ed model?
· Discuss the prefe
ed model. What does it tell you about predicting sales prices? Where are units pricey, and why? Where are they cheaper and why? What is expected or unexpected about your results? Is there anything interesting about it?
Using your chosen model:
· What is the estimate of the difference in price for a unit that was sold at the developer’s set price versus sold at auction, for a unit that has an unobstructed ocean view versus the unit across the hall?
· What is the estimate of the difference in price for a unit that was sold at the developer’s set price versus sold at auction, for a unit that has an unobstructed ocean view versus the unit across the hall? Consider this scenario again, but now compare units on the top floor vs on the ground floor.
· What unit would you like to purchase? What is attractive about it to you? What is it’s predicted price (Assume it was purchased at auction)? What characteristics does it have? What makes this unit more (or less) attractive than another unit (from an expected resale perspective)? Why do you say that?
HINT: You will find it very helpful to read Case 5, on pages 457 – 474 in your textbook. Please also look at Appendix B in your textbook.
3

Sheet 1 - CONDO
    CONDO
    PRICE100    FLOOR    DIST    VIEW    END    FURNISH    SOLD
    195    2    6    1    0    1    A
    180    3    6    1    0    1    A
    175    4    6    1    0    1    A
    175    8    6    1    0    1    A
    275    1    15    1    0    0    F
    249    2    15    1    0    0    F
    264    3    15    1    0    0    F
    259    4    15    1    0    0    F
    205    5    15    1    0    0    A
    294    6    15    1    0    0    F
    274    7    15    1    0    0    F
    279    8    15    1    0    0    F
    274    2    14    1    0    0    F
    279    3    14    1    0    0    F
    284    4    14    1    0    0    F
    195    6    14    1    0    0    A
    195    7    14    1    0    0    A
    195    8    14    1    0    0    A
    264    1    13    1    0    0    F
    261    2    13    1    0    0    F
    269    3    13    1    0    0    F
    195    5    13    1    0    0    A
    195    6    13    1    0    0    A
    264    1    12    1    0    0    F
    261    2    12    1    0    0    F
    254    3    12    1    0    0    F
    190    4    12    1    0    0    A
    190    7    12    1    0    0    A
    190    8    12    1    0    0    A
    240    1    11    1    0    0    F
    264    3    11    1    0    0    F
    190    6    11    1    0    0    A
    190    7    11    1    0    0    A
    205    8    11    1    0    0    A
    220    1    10    1    0    0    A
    195    2    10    1    0    0    A
    190    4    10    1    0    0    A
    175    6    10    1    0    0    A
    200    7    10    1    0    0    A
    200    8    10    1    0    0    A
    264    1    9    1    0    0    F
    190    2    9    1    0    0    A
    254    3    9    1    0    0    F
    195    7    9    1    0    0    A
    185    6    6    1    0    0    A
    269    3    5    1    0    0    F
    195    4    5    1    0    0    A
    190    7    5    1    0    0    A
    185    8    5    1    0    0    A
    235    1    4    1    0    0    F
    195    4    4    1    0    0    A
    200    7    4    1    0    0    A
    195    8    4    1    0    0    A
    259    1    3    1    0    0    F
    200    3    3    1    0    0    A
    200    4    3    1    0    0    A
    215    6    3    1    0    0    A
    259    1    2    1    0    0    F
    215    4    2    1    0    0    A
    220    6    2    1    0    0    A
    205    8    2    1    0    0    A
    210    1    1    1    0    0    A
    210    2    1    1    0    0    A
    200    3    1    1    0    0    A
    260    4    1    1    0    0    F
    205    5    1    1    0    0    A
    205    7    1    1    0    0    A
    190    8    1    1    0    0    A
    204    1    14    0    0    0    F
    209    2    14    0    0    0    F
    209    3    14    0    0    0    F
    199    4    14    0    0    0    F
    189    5    14    0    0    0    F
    199    6    14    0    0    0    F
    219    8    14    0    0    0    F
    204    1    13    0    0    0    F
    199    2    13    0    0    0    F
    163    3    13    0    0    0    F
    179    5    13    0    0    0    F
    190    8    13    0    0    0    F
    204    1    12    0    0    0    F
    159    2    12    0    0    0    F
    169    3    12    0    0    0    F
    190    6    12    0    0    0    F
    175    7    12    0    0    0    A
    190    8    12    0    0    0    F
    199    1    11    0    0    0    F
    190    2    11    0    0    0    F
    180    3    11    0    0    0    F
    176    4    11    0    0    0    F
    186    5    11    0    0    0    F
    170    6    11    0    0    0    A
    195    8    11    0    0    0    F
    175    1    10    0    0    0    F
    159    2    10    0    0    0    F
    165    3    10    0    0    0    F
    160    4    10    0    0    0    A
    175    8    10    0    0    0    A
    204    1    9    0    0    0    F
    159    6    9    0    0    0    F
    165    6    9    0    0    0    A
    170    7    9    0    0    0    A
    165    8    9    0    0    0    A
    199    1    6    0    0    0    F
    204    2    6    0    0    0    F
    169    3    6    0    0    0    F
    199    6    6    0    0    0    F
    175    8    6    0    0    0    A
    175    1    5    0    0    0    F
    151    2    5    0    0    0    F
    161    3    5    0    0    0    F
    175    8    5    0    0    0    A
    175    1    4    0    0    0    F
    151    2    4    0    0    0    F
    169    3    4    0    0    0    F
    165    6    4    0    0    0    A
    170    8    4    0    0    0    A
    199    1    3    0    0    0    F
    159    2    3    0    0    0    F
    169    3    3    0    0    0    F
    175    6    3    0    0    0    A
    175    8    3    0    0    0    A
    199    1    2    0    0    0    F
    159    2    2    0    0    0    F
    180    6    2    0    0    0    F
    175    8    2    0    0    0    A
    159    2    1    0    0    0    F
    169    3    1    0    0    0    F
    209    4    1    0    0    0    F
    165    6    1    0    0    0    A
    200    1    6    1    1    1    A
    195    5    6    1    1    1    A
    190    7    6    1    1    1    A
    306    3    15    1    1    0    F
    164    5    14    1    1    0    F
    250    7    14    1    1    0    F
    295    1    13    1    1    0    F
    215    7    13    1    1    0    F
    205    8    13    1    1    0    A
    279    4    12    1    1    0    F
    205    5    12    1    1    0    A
    200    6    12    1    1    0    A
    205    4    11    1    1    0    A
    205    5    11    1    1    0    A
    205    3    10    1    1    0    A
    265    3    10    1    1    0    F
    255    4    10    1    1    0    F
    205    5    10    1    1    0    A
    200    4    9    1    1    0    A
    195    5    9    1    1    0    A
    200    6    9    1    1    0    A
    205    8    9    1    1    0    A
    225    1    5    1    1    0    A
    220    2    5    1    1    0    A
    200    5    5    1    1    0    A
    182    6    5    1    1    0    F
    225    2    4    1    1    0    A
    265    5    4    1    1    0    A
    200    6    4    1    1    0    A
    231    7    4    1    1    0    F
    225    2    3    1    1    0    A
    220    5    3    1    1    0    A
    215    8    3    1    1    0    A
    210    3    2    1    1    0    A
    215    5    2    1    1    0    A
    249    4    1    1    1    0    F
    140    5    1    1    1    0    F
    230    1    14    0    1    0    F
    206    2    13    0    1    0    F
    217    2    13    0    1    0    F
    150    3    13    0    1    0    F
    199    2    12    0    1    0    F
    197    3    12    0    1    0    F
    166    5    12    0    1    0    A
    180    7    11    0    1    0    A
    189    8    11    0    1    0    F
    175    5    10    0    1    0    A
    170    6    10    0    1    0    A
    180    7    10    0    1    0    A
    160    5    9    0    1    0    A
    190    3    6    0    1    0    F
    185    5    6    0    1    0    A
    175    6    5    0    1    0    A
    190    8    5    0    1    0    F
    165    4    4    0    1    0    A
    177    4    4    0    1    0    F
    175    5    4    0    1    0    A
    175    7    4    0    1    0    A
    175    4    3    0    1    0    A
    186    7    3    0    1    0    A
    170    3    2    0    1    0    A
    175    4    2    0    1    0    A
    130    5    2    0    1    0    F
    180    6    2    0    1    0    A
    205    3    1    0    1    0    F
    202    4    1    0    1    0    F
    175    5    1    0    1    0    A
    175    5    1    0    1    0    F
    210    6    1    0    1    0    A
    215    6    1    0    1    0    F
    180    8    1    0    1    0    A
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Answered 1 days AfterApr 08, 2022

Solution

Manoj answered on Apr 09 2022
14 Votes
Model 1. First order multiple regression model.
We find that the value of R squared is 0.486. Which means multiple linear regression model explains 48.6% variability in the data.
The du
in Watson test statistic is 1.783, which is between two critical values (1.5,2.5). Therefore we can assume that there is first order auto-co
elation in our data. We find that the all values of Tolerance are greater than 0.1 and VIFs are close to 1. We can assume that there is no multicollinearity present in the data. Now we check the normality of residuals with a normal P-P plot. We can see that the all points follow the normal line with no deviations. It indicates that the residuals are normally distributed.
The first order multiple regression equation is given by,
y = 183.721 – 3.827(FLOOR) + 1.725 (DIST) + 40.133 (VIEW) + 4.183 (END) – 32.452 (FURNISH)
Now we check the significance of the model.
The p-value of F test is 0 which is less than the level of significance. We can reject the null hypothesis that and conclude that model provides a better fit than the intercept-only model.
The p-values of all independent variables expect END are less than level of significance. We can say that the variables FLOOR, DIST, VIEW and FURNISH are statistically significant. Independent variable END can be removed from the model since it is statistically insignificant.
Model 2: second order multiple regression for the quantitative variables.
Dependent Variable: Price: in hundreds of dollars (PRICE100)
Independent Variable:
                1. DIST
                2. DIST2
From the SPSS output, we find the value of R-Squared is 0.186, Which means multiple linear regression model explains 18.6% variability in the data.
The du
in Watson test statistic is1.294, which is between two critical values (1.5,2.5). Therefore we can assume that there is first order auto-co
elation in our data.
We see that the value of tolerance for DIST and DIST2 are less than 0.1and VIF for DIST and DIST2 are too high. Its means there is high co
elation between these two variables. There is a presence on multicollinearity. Now we check the normality of residuals with a normal P-P plot. We can see that the all points follow the normal line with no deviations. It indicates that the residuals are normally distributed.
The fitted second order multiple regression for the quantitative variables given by,
PRICE100 = 209.762 – 8.093(DIST) + 0.669 (DIST2)
T test for the significance of regression coefficient.
From the output, we can find that the p-values for all independent variables are less than the level of significance. We can say that these three variables are statistically significant.
F test for overall significance.
The null and alternative hypotheses of the F test given by,
Null Hypothesis: The model with no independent variables fits the data as well as your model.
Alternative Hypothesis: Model fits the data better than the intercept only model.
From the ANOVA table of output we find that the p-value of F test is 0 and which is less than the level of significance 0.05, we reject the null hypothesis. There is enough evidence to claim that Model...
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