0 200 400 600 800 1000 1200 1400 16000

10

20

30

40

50

60

70

80

90

f(x) = 0.0500802737347663 x − 2.36966012519268R² = 0.889245891351415

Scatter Plot

X

Y

Pies Horas545 24400 13.5562 26.25540 25220 9344 20569 22340 11.25900 50285 12865 38.75831 40344 19.5360 18750 28650 27415 21275 15557 25

1028 45793 29523 21564 22312 16.5757 37600 32796 34577 25500 31695 24

1054 40486 27442 18

1249 62.5995 53.75

1397 79.5

Confidence Interval Estimate

DataX Value 500Confidence Level 95%

Intermediate CalculationsSample Size 36Degrees of Freedom 34t Value 2.03224451XBar, Sample Mean of X 625.555556Sum of Squared Differences from XBar 2755432.89Standard Error of the Estimate 5.03142663h Statistic 0.03349891Predicted Y (YHat) 22.6704767

For Average YInterval Half Width 1.87146817 Para el promedio con una confianza del 95%, para la poblacion de traslados de 500 pies cubicos el tiempoConfidence Interval Lower Limit 20.7990086 que tomara esta entre 20.79 y 20.54 horasConfidence Interval Upper Limit 24.5419449

For Individual Response Y para el intervalo de prediccionInterval Half Width 10.3949431 preg b Con una confianza de 95%, pronosticamos que el tiempo para una mudanza de 500 pies 3Prediction Interval Lower Limit 12.2755337 sera entre 12.27 y33.06Prediction Interval Upper Limit 33.0654198

Respuesta Cel ancho es mayor para el intervalo indivual que para el promediopor una variacion para la variable individual

con una confianza del 95%, para la poblacion de traslados de 500 pies cubicos el tiempoque tomara esta entre 20.79 y 20.54 horas

Con una confianza de 95%, pronosticamos que el tiempo para una mudanza de 500 pies 3sera entre 12.27 y33.06

Durbin-Watson Calculations

Sum of Squared Difference of Residuals 1420.7670824Sum of Squared Residuals 860.71863323

Durbin-Watson Statistic 1.65067541

0 200 400 600 800 1000 1200 1400 1600-15

-10

-5

0

5

10

15

Residual Plot

X

Resid

uals

Pies Residuals545 -0.92408906400 -4.16244937562 0.47454629540 0.32631231220 0.3519999344 5.14204596569 -4.12601563340 -3.40763294900 7.29741376285 0.09678211865 -2.19977666831 0.75295265344 4.64204596360 2.34076158750 -7.19054518650 -3.1825178415 2.58634653275 3.59758485557 -0.52505235

1028 -4.11286127793 -8.34399695523 -2.82232304564 -3.87561426312 3.24461472757 1.45889291600 4.32149588796 -3.49423777577 -1.52665782500 8.32952326695 -8.43613012

1054 -10.4149484486 5.03064709442 -1.76582087

1249 2.31939823995 6.28978776

1397 11.9075177

Observation Pies Predicted Y Horas Residuals1 545 24.92408906 24 -0.92408906032 400 17.662449369 13.5 -4.16244936873 562 25.775453714 26.25 0.47454628634 540 24.673687692 25 0.32631230845 220 8.6480000965 9 0.35199990356 344 14.85795404 20 5.14204596047 569 26.12601563 22 -4.12601562998 340 14.657632945 11.25 -3.40763294469 900 42.702586236 50 7.2974137639

10 285 11.903217889 12 0.096782110811 865 40.949776655 38.75 -2.199776655412 831 39.247047348 40 0.752952651613 344 14.85795404 19.5 4.642045960414 360 15.659238419 18 2.340761580715 750 35.190545176 28 -7.190545175916 650 30.182517802 27 -3.182517802417 415 18.413653475 21 2.586346525318 275 11.402415152 15 3.597584848119 557 25.525052345 25 -0.525052345120 1028 49.112861274 45 -4.112861274121 793 37.343996946 29 -8.343996946522 523 23.822323038 21 -2.822323038123 564 25.875614261 22 -3.875614261224 312 13.25538528 16.5 3.244614719925 757 35.541107092 37 1.45889290826 600 27.678504116 32 4.321495884327 796 37.494237768 34 -3.494237767728 577 26.52665782 25 -1.526657819829 500 22.670476742 31 8.329523257830 695 32.43613012 24 -8.436130120531 1054 50.414948391 40 -10.41494839132 486 21.96935291 27 5.030647090133 442 19.765820866 18 -1.765820865634 1249 60.18060177 62.5 2.319398230535 995 47.460212241 53.75 6.289787759136 1397 67.592482282 79.5 11.907517718

Pies Horas545 24400 13.5562 26.25540 25220 9344 20569 22340 11.25900 50285 12865 38.75831 40344 19.5360 18750 28650 27415 21275 15557 25

1028 45793 29523 21564 22312 16.5757 37600 32796 34577 25500 31695 24

1054 40486 27442 18

1249 62.5995 53.75

1397 79.5

Simple Linear Regression Analysis

Regression StatisticsMultiple R 0.9430R Square 0.8892Adjusted R Square 0.8860Standard Error 5.0314Observations 36

ANOVAdf SS MS F Significance F

Regression 1 6910.7189 6910.7189 272.9864 0.0000Residual 34 860.7186 25.3153Total 35 7771.4375

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept -2.3697 2.0733 -1.1430 0.2610 -6.5830 1.8437Pies 0.0501 0.0030 16.5223 0.0000 0.0439 0.0562

Calculationsb1, b0 Coefficients 0.0501 -2.3697b1, b0 Standard Error 0.0030 2.0733R Square, Standard Error 0.8892 5.0314

272.9864 34.00006910.7189 860.7186

Confidence level 95%2.03224.21340.0062

Lower 95% Upper 95%-6.5830 1.843710.0439 0.05624

F, Residual dfRegression SS, Residual SS

t Critical ValueHalf Width b0Half Width b1

y xHoras Pies

24.00 54513.50 40026.25 56225.00 540

9.00 22020.00 34422.00 56911.25 34050.00 90012.00 28538.75 86540.00 83119.50 34418.00 36028.00 75027.00 65021.00 41515.00 27525.00 55745.00 102829.00 79321.00 52322.00 56416.50 31237.00 75732.00 60034.00 79625.00 57731.00 50024.00 69540.00 105427.00 48618.00 44262.50 124953.75 99579.50 1397