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How much does it cost? I’m in rush please give me details asap

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 preferred? Why? · Choose which model you think is best of all the models you developed. Why is this the preferred model? · Discuss the preferred 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 PRICE100FLOORDISTVIEWENDFURNISHSOLD 19526101A 18036101A 17546101A 17586101A 275115100F 249215100F 264315100F 259415100F 205515100A 294615100F 274715100F 279815100F 274214100F 279314100F 284414100F 195614100A 195714100A 195814100A 264113100F 261213100F 269313100F 195513100A 195613100A 264112100F 261212100F 254312100F 190412100A 190712100A 190812100A 240111100F 264311100F 190611100A 190711100A 205811100A 220110100A 195210100A 190410100A 175610100A 200710100A 200810100A 26419100F 19029100A 25439100F 19579100A 18566100A 26935100F 19545100A 19075100A 18585100A 23514100F 19544100A 20074100A 19584100A 25913100F 20033100A 20043100A 21563100A 25912100F 21542100A 22062100A 20582100A 21011100A 21021100A 20031100A 26041100F 20551100A 20571100A 19081100A 204114000F 209214000F 209314000F 199414000F 189514000F 199614000F 219814000F 204113000F 199213000F 163313000F 179513000F 190813000F 204112000F 159212000F 169312000F 190612000F 175712000A 190812000F 199111000F 190211000F 180311000F 176411000F 186511000F 170611000A 195811000F 175110000F 159210000F 165310000F 160410000A 175810000A 20419000F 15969000F 16569000A 17079000A 16589000A 19916000F 20426000F 16936000F 19966000F 17586000A 17515000F 15125000F 16135000F 17585000A 17514000F 15124000F 16934000F 16564000A 17084000A 19913000F 15923000F 16933000F 17563000A 17583000A 19912000F 15922000F 18062000F 17582000A 15921000F 16931000F 20941000F 16561000A 20016111A 19556111A 19076111A 306315110F 164514110F 250714110F 295113110F 215713110F 205813110A 279412110F 205512110A 200612110A 205411110A 205511110A 205310110A 265310110F 255410110F 205510110A 20049110A 19559110A 20069110A 20589110A 22515110A 22025110A 20055110A 18265110F 22524110A 26554110A 20064110A 23174110F 22523110A 22053110A 21583110A 21032110A 21552110A 24941110F 14051110F 230114010F 206213010F 217213010F 150313010F 199212010F 197312010F 166512010A 180711010A 189811010F 175510010A 170610010A 180710010A 16059010A 19036010F 18556010A 17565010A 19085010F 16544010A 17744010F 17554010A 17574010A 17543010A 18673010A 17032010A 17542010A 13052010F 18062010A 20531010F 20241010F 17551010A 17551010F 21061010A 21561010F 18081010A &"Helvetica Neue,Regular"&12&K000000&P

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 preferred? Why? · Choose which model you think is best of all the models you developed. Why is this the preferred model? · Discuss the preferred 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 PRICE100FLOORDISTVIEWENDFURNISHSOLD 19526101A 18036101A 17546101A 17586101A 275115100F 249215100F 264315100F 259415100F 205515100A 294615100F 274715100F 279815100F 274214100F 279314100F 284414100F 195614100A 195714100A 195814100A 264113100F 261213100F 269313100F 195513100A 195613100A 264112100F 261212100F 254312100F 190412100A 190712100A 190812100A 240111100F 264311100F 190611100A 190711100A 205811100A 220110100A 195210100A 190410100A 175610100A 200710100A 200810100A 26419100F 19029100A 25439100F 19579100A 18566100A 26935100F 19545100A 19075100A 18585100A 23514100F 19544100A 20074100A 19584100A 25913100F 20033100A 20043100A 21563100A 25912100F 21542100A 22062100A 20582100A 21011100A 21021100A 20031100A 26041100F 20551100A 20571100A 19081100A 204114000F 209214000F 209314000F 199414000F 189514000F 199614000F 219814000F 204113000F 199213000F 163313000F 179513000F 190813000F 204112000F 159212000F 169312000F 190612000F 175712000A 190812000F 199111000F 190211000F 180311000F 176411000F 186511000F 170611000A 195811000F 175110000F 159210000F 165310000F 160410000A 175810000A 20419000F 15969000F 16569000A 17079000A 16589000A 19916000F 20426000F 16936000F 19966000F 17586000A 17515000F 15125000F 16135000F 17585000A 17514000F 15124000F 16934000F 16564000A 17084000A 19913000F 15923000F 16933000F 17563000A 17583000A 19912000F 15922000F 18062000F 17582000A 15921000F 16931000F 20941000F 16561000A 20016111A 19556111A 19076111A 306315110F 164514110F 250714110F 295113110F 215713110F 205813110A 279412110F 205512110A 200612110A 205411110A 205511110A 205310110A 265310110F 255410110F 205510110A 20049110A 19559110A 20069110A 20589110A 22515110A 22025110A 20055110A 18265110F 22524110A 26554110A 20064110A 23174110F 22523110A 22053110A 21583110A 21032110A 21552110A 24941110F 14051110F 230114010F 206213010F 217213010F 150313010F 199212010F 197312010F 166512010A 180711010A 189811010F 175510010A 170610010A 180710010A 16059010A 19036010F 18556010A 17565010A 19085010F 16544010A 17744010F 17554010A 17574010A 17543010A 18673010A 17032010A 17542010A 13052010F 18062010A 20531010F 20241010F 17551010A 17551010F 21061010A 21561010F 18081010A &"Helvetica Neue,Regular"&12&K000000&P

Answered 1 days AfterApr 08, 2022

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 durbin Watson test statistic is 1.783, which is between two critical values (1.5,2.5). Therefore we can assume that there is first

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 durbin 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-correlation 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 correlation 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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