Price Bedroom Bathroom Cars SQ FT 298,000 3 2.5 0 1,566 319,900 3 2.5 0 2,000 354,000 3 2 2 0 374,900 4 2.5 0 2,816 385,000 4 2 0 0 389,000 3 2.5 0 2,248 399,000 4 3 0 2,215 415,000 3 2.5 0 3,188 444,900 3 2 0 2,530 450,000 3 2 0 1,967 465,000 4 3 0 2,564 340,000 4 2.5 0 2,293 275,000 3 2.5 2 1,353 425,000 3 2 0 1,834 250,000 3 2.5 0 5,837 450,000 3 2.5 0 9,060 390,000 3 3.5 0 1,002 269,000 3 2.5 0 1,680 425,000 3 2.5 2 4,356 425,000 2 2.5 2 2,993 425,000 3 3 0 4,356 429,900 5 3.5 1 2,154 400,000 3 2.5 2 1,846 399,900 3 2 1 2,018 388,990 4 4 0 2,295 Plz do all the calculations on excel n show the excel files. Construct and interpret a...

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PriceBedroomBathroomCarsSQ FT
298,00032.501,566
319,90032.502,000
354,0003220
374,90042.502,816
385,0004200
389,00032.502,248
399,0004302,215
415,00032.503,188
444,9003202,530
450,0003201,967
465,0004302,564
340,00042.502,293
275,00032.521,353
425,0003201,834
250,00032.505,837
450,00032.509,060
390,00033.501,002
269,00032.501,680
425,00032.524,356
425,00022.522,993
425,0003304,356
429,90053.512,154
400,00032.521,846
399,9003212,018
388,9904402,295
  1. Plz do all the calculations on excel n show the excelfiles.
  2. Construct and interpret a correlation matrix for yourdata.
  3. Show the step wise process of determining the best regressionmodel to predict PRICE Anova. EXPLAIN the process as you move fromthe full model to your final model.
  4. Using your final model, select values for the independentvariables and predict the house’s sales price.

Answer & Explanation Solved by verified expert
3.7 Ratings (665 Votes)

data -> data analysis -> correlation

Price Bedroom Bathroom Cars SQ FT
Price 1
Bedroom 0.1163 1
Bathroom 0.0693 0.4544 1
Cars -0.0119 -0.2773 -0.1439 1
SQ FT 0.1825 -0.1648 0.0818 -0.1522 1

with Price, there is weak correlation with every independent variable

SQ FT has highest correlation with Price

Bathroom and Bedroom has correlation 0.4544

data -> data analysis -> regression

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.2449
R Square 0.0600
Adjusted R Square -0.1280
Standard Error 63973.0562
Observations 25
ANOVA
df SS MS F Significance F
Regression 4 5222651649.4394 1305662912.3598 0.3190 0.8619
Residual 20 81851038446.5607 4092551922.3280
Total 24 87073690096.0000
Coefficients Standard Error t Stat P-value Lower 95%
Intercept 309980.8034 90752.3457 3.4157 0.0027 120674.7275
Bedroom 17945.0381 25574.4249 0.7017 0.4910 -35402.2774
Bathroom -2595.9465 28990.2834 -0.0895 0.9295 -63068.6180
Cars 5114.6350 16897.8149 0.3027 0.7653 -30133.5893
SQ FT 7.3638 7.4722 0.9855 0.3362 -8.2230

see the column p-value

we observe that all independent variable has p-value > 0.05

hence they all are insignificant

lowest p-value is of SQ Ft

so we just include this

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.182509324
R Square 0.033309653
Adjusted R Square -0.008720362
Standard Error 60495.52468
Observations 25
ANOVA
df SS MS F Significance F
Regression 1 2900394438 2900394438 0.792520615 0.382556796
Residual 23 84173295658 3659708507
Total 24 87073690096
Coefficients Standard Error t Stat P-value Lower 95%
Intercept 368173.8051 21041.84324 17.49722213 8.72146E-15 324645.4359
SQ FT 5.970685713 6.706855155 0.890236269 0.382556796 -7.903501245

y^ = 368173.8051 + 5.9707 SQ FT


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