I need to complete part a and e for this question
Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) X File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share & Cut Aria - 9 A A Wrap Text General EX AutoSum ~ AO Copy Fill Paste 6.0 .00 Sort & Find & Format Painter BIU -.LA Merge & Center . $ + % " .00 .0 Conditional Format as Cell Insert Delete Format Clear Formatting Table Styles Filter * Select Clipboard Font Alignment Number Styles Cells Editing Q35 X v fx b. Predictors: (Constant), LOT_SIZE, BEDROOMS, FAMILYRM, FIREPLCS, BATHS, LIVAREA Q R S T U V W X Y Z AA AB AC 18 Variables Entered/Removed Variables Entered/Removed 19 Model Variables Entered Variables Removed Method Mode Variables Entered Variables Removed Method LOT_SIZE, DEPTH, BEDROOMS, BEDROOMS, FAMILYRM, Enter FAMILYRM, Enter FIREPLCS, BATHS, FIREPLCS, BATHS, LIVAREA LIVAREA, LOT_SIZE 20 21 a. Dependent Variable: PRICE a. Dependent Variable: PRICE 22 b. All requested variables entered. b. All requested variables entered. 23 24 Model Summary Model Summary Adjusted R Std. Error of the Adjusted R Std. Error of the 25 Model R R Square Square Estimate Mode R R Square Square Estimate 26 3 .806 650 642 50359.390 308 .652 643 50305.898 a. Predictors: (Constant), LOT_SIZE, BEDROOMS, FAMILYRM, FIREPLCS, BATHS, LIVAREA a. Predictors: (Constant), DEPTH, BEDROOMS, BATHS, LOT_SIZE, FAMILYRM, FIREPLCS 27 LIVAREA 28 29 ANOVA ANOVA 30 Model Sum of Squares df Mean Square F Sig Mode Sum of Squares df Mean Square 31 Regression 1 193076384331.620 198846064055.270 78.407 .000 Regression 1 196969432620.860 170995633231.552 32 Residual 641625247052.997 253 2536068170.170 Residual 637732198763.751 252 2530683328.428 33 Total 1834701631384.620 259 Total 1834701631384.620 259 Data MRA + Ready + 100% 6:31 PM Type here to search @ 26. C ~ D (7. 41) ENG 28/7/2021Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) X File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share & Cut Aria - 9 A A Wrap Text General AutoSum ~ AO Copy Fill Paste BIU -.LA Merge & Center . $ ~ % " 6.0 .00 .00 .0 Conditional Format as Cell Insert Delete Format Clear Sort & Find & Format Painter Formatting * Table Styles Filter * Select Clipboard Font Alignment Number Styles Cells Editing Q35 X V fx b. Predictors: (Constant), LOT_SIZE, BEDROOMS, FAMILYRM, FIREPLCS, BATHS, LIVAREA P Q R S U V W X Y Z AA AB AC 2 in columns Y through AE - W 5 Regression 3 Regression 4 Descriptive Statistics Descriptive Statistics 8 Mean Std. Deviation N Mean Std. Deviation N PRICE 41 1958.46 34165.255 260 PRICE 41 1958.46 84165.255 260 BEDROOMS 507 260 BEDROOMS 10 2.85 2.85 507 260 BATHS 1.6798 55414 260 BATHS 1.6798 55414 260 11 12 FAMILYRM 80 535 260 FAMILYRM 80 535 260 13 FIREPLCS 1.15 730 260 FIREPLCS 1.15 730 260 14 LIVAREA 1640.57 428.507 260 LIVAREA 1640.57 428.507 260 15 LOT_SIZE 7411.02 1688.431 260 LOT_SIZE 7411.02 1688.431 260 16 DEPTH 117.819 11.1583 260 17 18 Variables Entered/Removed Variables Entered/Removed 19 Model Variables Entered Variables Removed Method Model Variables Entered Variables Removed Method 3 LOT_SIZE, DEPTH BEDROOMS, BEDROOMS, Data MRA (+ + 100% Ready Q J - 6:30 PM Type here to search 260C ~ 0(7. 1') ENG 28/7/2021X Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share & Cut Aria - 10 A A Wrap Text General EX AutoSum . AO Fill Copy - Paste Format Painter BIU - - LA- Merge & Center - $ - % " 6.0 .00 Conditional Format as Cell Insert Delete Format Clear Sort & Find & .00 .0 Formatting * Table Styles Filter * Select Clipboard Font Alignment Number Styles Cells Editing M12 X V fx A B C D E F G H K M N 18 Variables Entered/Removed Variables Entered/Removed 19 Model Variables Entered Variables Removed Method Mode Variables Entered Variables Removed Method LOT_SIZE, LOT_SIZE, LIVAREAD Enter BEDROOMS Enter BATHS, LIVAREA 20 21 a. Dependent Variable: PRICE a. Dependent Variable: PRICE 22 b. All requested variables entered. b. All requested variables entered. 23 24 Model Summary Model Summary Adjusted R Std. Error of the Adjusted R Std. Error of the R R Square Square Estimate Mode R R Square Square Estimate 25 Model 26 772 595 592 53755.818 797 .636 .630 51175.836 a. Predictors: (Constant), LOT_SIZE, LIVAREA a. Predictors: (Constant), LOT_SIZE, BEDROOMS, BATHS, LIVAREA 27 28 29 ANOVA ANOVA 30 Model Sum of Squares Mean Square F Sig. Mode Sum of Squares df Mean Square F 2 4 291716315556.586 111.386 31 Regression 1092051816307.490 2 546025908153.745 188.957 .000 Regression 1 166865262226.340 Residual 742649815077.126 257 2889687996.409 Residual 667836369158.271 255 32 2618966153.562 Total 1834701631384.620 259 Total 1834701631384.620 259 33 Data MRA + + 100% Ready 6:30 PM Type here to search @ 260 C ~ D (7. ") ENG 28/7/2021X Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share Cut - 10 A A Wrap Text General EX Aria AutoSum . AO Fill Copy BIU . . DA BEE Merge & Center $ + % " 6.0 .00 Insert Delete Format Sort & Find & Paste .00 .0 Conditional Format as Cell Clear Format Painter Formatting * Table Styles Filter * Select Clipboard Font Alignment Number Styles Cells Editing M12 X V fx B C E G K M A D F H N 1 2 NOTE: Regression 1 is in columns A through G; Regression 2 is in columns I through O; Regression 3 is in columns Q through W; Regression - W Regression 1 Regression 2 Descriptive Statistics Descriptive Statistics Std. Deviation N Mear Std. Deviation N 00 Meal PRICE 41 1958.46 84165.255 260 PRICE 411958.46 84165.255 260 10 LIVAREA 1640.57 428.507 260 BEDROOMS 2.85 507 260 11 LOT_SIZE 7411.02 1688.431 260 BATHS 1.6798 55414 260 12 LIVAREA 1640.57 428.507 260 13 LOT SIZE 7411.02 1688.431 260 14 15 16 17 18 Variables Entered/Removed Variables Entered/Removed 19 Model Variables Entered Variables Removed Method Model Variables Entered Variables Removed Method 2 LOT SIZE, Data MRA + O J - + 100% Ready 6:29 PM Type here to search @ 260C ~ D0 (7. 1 1) ENG 28/7/2021+ g Headings that you add to the document will appear here. 11 12 13 I4 15 16 17 18 .2. .1.|.' .1-l 2.|.3.. d- -5ll-o .-7-|-3 llg 1o:11..12- 13' '14.n.15.|.15-*l17l|-1a.:.- the regression line. This Indicates that the regression line model Is not very accurate and could be more precise. f} ESTASR: ESTIMATEIPRICEW 00 'E-E-m ESTASR compare to the median and standard deviation of the other ASR models: As we have 2E0 data' having a 17 SD Is not that significant However It shows that there are some data that have a large spread of values away tram the mean. MRKTASR and COSTASR both have the same SD and Median which with a good estimation are the same as ESTASR. ORECASR have the minimum SD of all. which Indicates that they are clustered closely around the mean. All four assessment sales ratios here have slrnllarty the same Median, with ORIGASR about 15 units less than others. These results show that using only tlving area as the independent variable could be a good predictive model for sale price. The reason Is that all four assessment sales ratios with a pretty ' good estimate have the same median and standard deviation from the mean' 21 1 1 1 1 1 1 2 1 1 13 1 1 4 1 1 15 1 161 1 17 1 1 1 8 1 1 9 1 . 10 1 11 . 1 12 1 . 13 1 14 1 15 1 16 17 1 18 Question 4 a) The best prediction for the price of a property based on the square footage of the living area Headings that you add to the document of the house is a regression equation as follows: will appear here. Regression Equation : Price=148.474x+168376.529 b) Predicted value for property:3000 square feet of living area = $613,798.529 c) Predicted value for property:3000 square feet of living area = $613,787 Scatterplot with PRICE on the Y axis and LIVAREA on the X axis. + LIVAREA y = 148.47x + 168377 700,000 600,000 500,000 400,000 Price 300,000 200,000 Type here to search C 26 C A D (7 () ENG 6:40 PM 28/7/2021X Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share & Cut Wrap Text General EX - 9 A A AutoSum ~ AO Aria Fill Copy Sort & Find & Paste Format Painter BIU -.LA Merge & Center . $ + % " 6.0 .00 Conditional Format as Cell Insert Delete Format .00 .0 Clear Formatting Table Styles Filter * Select Font Alignment Styles Cells Clipboard Number Editing Q35 X v fx b. Predictors: (Constant), LOT_SIZE, BEDROOMS, FAMILYRM, FIREPLCS, BATHS, LIVAREA Q R S T U V W X Y Z AA AB AC 28 29 ANOVA ANOVA Sum of Squares Mean Square 30 Model Sum of Squares Mean Square F Sig Model 6 198846064055.270 78.407 .000" Regression 1 196969432620.860 170995633231.552 31 3 Regression 1193076384331.620 32 Residual 641625247052.997 253 2536068170.170 Residual 637732198763.751 252 2530683328.428 Total 1834701631384.620 259 33 Total 1834701631384.620 259 34 a. Dependent Variable: PRICE a. Dependent Variable: PRICE 35 b. Predictors: (Constant), LOT_SIZE, BEDROOMS, FAMILYRM, FIREPLCS, BATHS, LIVAREA b. Predictors: (Constant), DEPTH, BEDROOMS, BATHS, LOT_SIZE, FAMILYRM, FIREPLCS, LIVARE 36 37 Coefficients Coefficients Standardized Standardized Coefficients 38 Unstandardized Coefficients Coefficients Unstandardized Coefficients Beta 39 Model B Std. Error Bet Model B Sig. Std. Error (Constant 72552.135 22582.761 3.213 001 (Constant 109742.509 37523.333 3 40 BEDROOMS 30394.954 6740.116 183 4.510 .000 BEDROOMS 30882.849 6744.438 186 41 18299.725 8088.722 .120 2.262 025 BATHS 18055.867 8082.522 119 42 BATHS 43 FAMILYRM 4229.055 7228.230 027 585 .559 FAMILYRM 5093.574 7254.117 032 44 FIREPLCS 17499.728 5576.331 152 3.138 .002 FIREPLCS 17793.825 5575.452 .154 45 LIVAREA 87.852 11.946 447 7.354 .000 LIVAREA 86.965 11.954 443 46 LOT SIZE 7.319 1.978 . 147 3.700 .000 LOT_SIZE 8.625 2.239 173 DEPTH -402.550 324.559 -.053 47 a. Dependent Variable: PRICE a. Dependent Variable: PRICE Data MRA + O J - + 100% Ready 6:31 PM Type here to search 20 C 260C ~ 0 (7. (1) ENG 28/7/2021Research Project Assessment Database (1).xlsx - Excel (Product Activation Failed) X File Home Insert Page Layout Formulas Data Review View Tell me what you want to do. Sign in & Share & Cut Aria - 10 - A A Wrap Text General EX AutoSum . AO Copy Fill Paste BIU - - LA- BEMerge & Center . $ - % " 6.0 .00 .00 .0 Conditional Format as Cell Insert Delete Format Sort & Find & Format Painter Clear Formatting * Table Styles Filter * Select Clipboard Font Alignment Number Styles Cells Editing M12 X v fx A B C D E F G H K M N 26 772 595 592 53755.818 2 797 636 630 51175.836 a. Predictors: (Constant), LOT_SIZE, LIVAREA a. Predictors: (Constant), LOT_SIZE, BEDROOMS, BATHS, LIVAREA 27 28 29 ANOVA ANOVA 30 Model Sum of Squares df Mean Square F Sig. Mode Sum of Squares alf Mean Square F 31 Regression 1092051816307.490 2 546025908153.745 188.957 .000 2 Regression 1 166865262226.340 4 291716315556.586 111.386 32 Residual 742649815077.126 257 2889687996.409 Residual 667836369158.271 255 2618966153.562 33 Total 1834701631384.620 259 Total 1834701631384.620 259 34 a. Dependent Variable: PRICE a. Dependent Variable: PRICE 35 b. Predictors: (Constant), LOT_SIZE, LIVAREA b. Predictors: (Constant), LOT_SIZE, BEDROOMS, BATHS, LIVAREA 36 37 Coefficients Coefficients Standardized Standardized 38 Unstandardized Coefficients Coefficients Unstandardized Coefficients Coefficients 39 Model B Std. Error Beta t sig Model B Std. Error Beta 40 (Constant) 125149.348 17271.167 7.246 0OC 2 Constant) 53872.716 22107.531 2.437 41 LIVAREA 138. 103 8.239 .703 16.762 BEDROOMS 31404.828 6841.387 189 4.590 42 LOT_SIZE 8.129 2.091 163 3.888 .000 BATHS 23892.937 7836.937 157 3.049 43 a. Dependent Variable: PRICE LIVAREA 102.683 10.979 523 9.353 44 LOT_SIZE 8.078 1.994 162 4.052 45 a. Dependent Variable: PRICE Data MRA + Ready Q J - + 100% 6:30 PM Type here to search 20 C 260C ~ 0 ( 41) ENG 28/7/20212 1 71 1 1 2 1 1 13 1 1 4 1 151 6 1 17 19 1 1 .10 1 11 1 12 1 . 13 1 14 1 15 1 16 17 1 18 d) They are not different because the linear regression function aims to minimize the sum of squares around the data. So the best fit line that we draw will help all the points around the line to be the closest to the line as possible. Headings that you add to the document e) The standard error of the estimate (SEE) : $55,206.527. This measures the amount of will appear here. deviation between the actual and predicted sale price. This shows that the predicted price could be out by a margin of +55,206.527 or -55,206.527. SEE as a percentage of the mean (the coefficient of variation): Mean of LIVAREA: 1640.565 + Coefficient of variation: SEE/Mean = 55,206.527/1640.565 = 33.65 It is better to have a coefficient of variation for less than 30, here we have 33.65 which is considered as a high CV. Here the standard error of estimate is more than 30% of the mean of the dependent variable(living area), which shows the data points are loosely clustered around the regression line. This indicates that the regression line model is not very accurate and could be more precise. f) ESTASR: ESTIMATE/PRICE*100 ORIGASR COSTASR MRKTASR ESTASR F SD. 7.61 12.47 11.77 17.73 Type here to search 9 C 26'C A D ( () ENG 6:41 PM 28/7/2021An appraisal specialist has applied multiple regression analysis to prepare a mass appraisal model for this Edmonton data. Four variations of this model have been prepared, all using PRICE as the dependent variable. The model results are provided in the "MRA" worksheet in the "Project 2: Assessment Database" file. (a) Compare the results of Regression 1 with the results obtained in Question 4. What are the main differences? Which results are better? (Hint: consider the descriptive statistics for the two regression results). (5 marks) (b) Using the results for Regressions 1, 2, and 3, obtain a predicted value for a property with 2,500 square feet of living area, a 7,200 square foot lot, 3 bedrooms, 2 bathrooms, 2 fireplaces, and 1 family room. Why are they different? (4 marks) (c) Which variables in Regression 3 are the most important? Why? List the top five variables in decreasing order of importance. (2 marks) (d) In Regression 4 there are two variables that are related to the lot C LOT_SIZE and either WIDTH or DEPTH. How would you explain the coefficient for the WIDTH or DEPTH variable? If the results are different than expected, explain why. (4 marks) (e) Which of the four regression results do you consider to be the best and why? In your answer, consider the statistics for each regression (R , SEE, F-value, and COV) and the coefficients and t- statistics for each variable. Include a table summarizing your comparison of the four regression results. (Hint: calculate the COV for each regression). (10 marks) (f) NOT FOR MARKS: What are the benefits and drawbacks of using this multiple regression analysis model for property appraisal versus traditional appraisal methods? This illustrates the most important step of all C seeing how this statistical analysis may be applied in real estate practice. Interested students should consider the BUSI 344 course, "Statistical and Computer Applications in Real Estate"
