please assist with the NON GRADED problem. Data file is shown below:Selling PriceBathsSq FtBedrooms1600001.5177631700002176831780001121931825001156821951001.5112532125002119622459002212832500003128032550002159632580003.5237442670002.5243932680002147042750002167842950002.5186033250003205643250003.5277643284002140843310001.5197233445002.5173633650002.5199043850002.5364043950002.5191843990002210834300002246244300002261544540003.537004 b. The Excel output for the estimated regression equation that can be used to predict selling price given square footage and the number of bedrooms:SUMMARY OUTPUTRegression statisticsMultiple R0.7428R Square0.5518Adjusted R Square0.5128Standard Error60591.9567Observations26ANOVAdfMSFSignificance FRegression2 1.03955E+115197726551614.157399019.81929E-05Residual23 844418601223671385223Total25 1.88396E+11CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Intercept-5882.762265587.6835-0.08970.9293 -141561.2229129795.6985Sq Ft59.733121.27072.80820.010015.7313103.7349Beds54309.208322101.62312.45720.02208588.5174100029.8991Compare the fit for this simpler model to that of the model that also includes number of bathrooms as an independent variable.The adjusted R² for the simpler model is(to 2 decimals) that is higherthan the adjusted R of the model in part a. Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website)Click on the datafile logo to reference the data.DATA filea. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house:SUMMARY OUTPUTRegression statisticsMultiple R0.7429R Square0.5519Adjusted R Square0.4907Standard Error61948.6931Observations26ANOVAdfMSFSignificance FRegression31.0397E+113.4656E+109.0306E+004.3455E-04Residual228.4428E+103.8376E+09Total251.8840E+11CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Intercept-5531.014467312.9506-0.08220.9353 -145129.5298134067.5011Baths-1386.210023143.8052-0.05990.9528-49383.524346611.1044Sq Ft60.279323.58132.55620.018011.3748109.1838Beds54797.077824019.75922.28130.03264983.1461104611.0095Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R, is greater than 45%, the estimated regression equation provides a good fit.The estimated regression equation doesprovide a reasonable fit because the adjusted R is(to 2 decimals).

Name: please assist with the NON GRADED problem. Data file is shown below:Se
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Description: please assist with the NON GRADED problem. Data file is shown below:Selling PriceBathsSq FtBedrooms1600001.5177631700002176831780001121931825001156821951001.5112532125002119622459002212832500003128032550002159632580003.5237442670002.52439326800021470

a. From the output, the Adjusted R-square is 0.49 Answer: The estimated regression equation does…

Answered: please assist with the NON GRADED…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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please assist with the NON GRADED problem. Data file is shown below:

Selling Price	Baths	Sq Ft	Bedrooms
160000	1.5	1776	3
170000	2	1768	3
178000	1	1219	3
182500	1	1568	2
195100	1.5	1125	3
212500	2	1196	2
245900	2	2128	3
250000	3	1280	3
255000	2	1596	3
258000	3.5	2374	4
267000	2.5	2439	3
268000	2	1470	4
275000	2	1678	4
295000	2.5	1860	3
325000	3	2056	4
325000	3.5	2776	4
328400	2	1408	4
331000	1.5	1972	3
344500	2.5	1736	3
365000	2.5	1990	4
385000	2.5	3640	4
395000	2.5	1918	4
399000	2	2108	3
430000	2	2462	4
430000	2	2615	4
454000	3.5	3700	4

b. The Excel output for the estimated regression equation that can be used to predict selling price given square footage and the number of bedrooms:
SUMMARY OUTPUT
Regression statistics
Multiple R
0.7428
R Square
0.5518
Adjusted R Square
0.5128
Standard Error
60591.9567
Observations
26
ANOVA
df
MS
F
Significance F
Regression
2 1.03955E+11
51977265516
14.15739901
9.81929E-05
Residual
23 84441860122
3671385223
Total
25 1.88396E+11
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-5882.7622
65587.6835
-0.0897
0.9293 -141561.2229
129795.6985
Sq Ft
59.7331
21.2707
2.8082
0.0100
15.7313
103.7349
Beds
54309.2083
22101.6231
2.4572
0.0220
8588.5174
100029.8991
Compare the fit for this simpler model to that of the model that also includes number of bathrooms as an independent variable.
The adjusted R² for the simpler model is
(to 2 decimals) that is higher
than the adjusted R of the model in part a.

Transcribed Image Text:Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website) Click on the datafile logo to reference the data. DATA file a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house: SUMMARY OUTPUT Regression statistics Multiple R 0.7429 R Square 0.5519 Adjusted R Square 0.4907 Standard Error 61948.6931 Observations 26 ANOVA df MS F Significance F Regression 3 1.0397E+11 3.4656E+10 9.0306E+00 4.3455E-04 Residual 22 8.4428E+10 3.8376E+09 Total 25 1.8840E+11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -5531.0144 67312.9506 -0.0822 0.9353 -145129.5298 134067.5011 Baths -1386.2100 23143.8052 -0.0599 0.9528 -49383.5243 46611.1044 Sq Ft 60.2793 23.5813 2.5562 0.0180 11.3748 109.1838 Beds 54797.0778 24019.7592 2.2813 0.0326 4983.1461 104611.0095 Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R, is greater than 45%, the estimated regression equation provides a good fit. The estimated regression equation does provide a reasonable fit because the adjusted R is (to 2 decimals).

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