CON 370 Data Sets and Outputs · Version 1.1 . Learn. Perform. Succeed. CON 370 Data Sets and...

Version 1.1

Learn. Perform. Succeed.

CON 370 Data Sets and Outputs

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Table of Contents Personnel Shelters (descriptive statistics) ...................................................................................... 5

Personnel Shelters (regression) ...................................................................................................... 7

Depot Reparables (regression) ..................................................................................................... 39

Airborne Radios (regression) ........................................................................................................ 45

Work Crew Productivity (regression) ............................................................................................ 53

Aircraft Engines (regression) ......................................................................................................... 57

Wheel & Brake Assembly Spares (dummy variables) ................................................................... 67

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Price ($K)3.7504.4555.8506.9997.44911.32512.95013.78014.25016.98517.82018.96519.45019.98521.420

Personnel Shelters (descriptive statistics) You have received an urgent request for personnel shelters. Of the commercial vendors that normally provide such shelters only one vendor is capable of meeting the specifications, quantity, and the delivery dates. Use the historical data that you have collected to determine if the contractor’s quote of $14,999 is fair and reasonable.

If you used the mean price as your estimate, what would be the average estimating error, both in dollars and as a percentage?

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Price3.750 Price Bins Frequency4.455 8.1675 55.850 Mean 13.02887 12.585 16.999 Standard Error 1.56977 17.0025 47.449 Median 13.78 21.42 5

11.325 Mode #N/A More 012.950 Standard Deviation 6.07969213.780 Sample Variance 36.9626514.250 Kurtosis -1.4537616.985 Skewness -0.2279617.820 Range 17.6718.965 Minimum 3.7519.450 Maximum 21.4219.985 Sum 195.43321.420 Count 15

Bins 4Range 17.67Bin Width 4.418

Bins8.168

12.58517.00321.420

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8.1675 12.585 17.0025 21.42 More

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Histogram

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Personnel Shelters (regression)

You have received an urgent request for personnel shelters. Of the commercial vendors that normally provide such shelters only one vendor is capable of meeting the specifications, quantity, and the delivery dates. The item manager has identified square footage, number of power receptacles and the weather rating as key drivers in the cost of shelters. Use the historical data below that you have collected to determine if the contractor’s quote of $14,999 for a 625 square foot shelter with 18 power receptacles and a weather rating of 50 is fair and reasonable.

Price ($K) Square

Footage Power

Receptacles Weather

Rating $3.750 85 2 20 $4.455 170 3 25 $5.850 200 4 35 $6.999 295 6 35 $7.449 355 8 40

$11.325 415 10 45 $12.950 450 12 50 $13.780 510 14 50 $14.250 600 16 50 $16.985 650 18 50 $17.820 745 22 50 $18.965 725 24 55 $19.450 790 26 50 $19.985 840 28 55 $21.420 875 32 55

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Interview with the Logistics lead for the Personnel Shelters: Larger shelters are obviously more expensive, but we generally don’t get a break on price per square foot because the larger shelters require more reinforcement and structure. The number of power receptacles drives the wiring requirements. There is a certain amount of fixed wiring required anytime you have power receptacles. Sure, additional receptacles drive up the price, but we generally get some discounts when we add more outlets. The wind or weather rating relates to the severity of the conditions that the shelter can take. The higher the rating of the structure, the tougher the structure is, and consequently the higher the price of the shelter. But I got to tell you, as you start pushing the high end of the weather ratings the prices can really go up. How would you characterize the relationships between price and each of these characteristics? Square Footage # of Power Receptacles Weather Rating

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Pairwise Variable Analysis For Dataset Shelters

I. Correlation Matrix

PRICE SQFT PWRREC WindPRICE 1.0000 0.9890 0.9779 0.9204SQFT 0.9890 1.0000 0.9841 0.9101PWRREC 0.9779 0.9841 1.0000 0.8568Wind 0.9204 0.9101 0.8568 1.0000

II. Scatter Plot

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PRICE Vs. SQFT Correlation Coefficient = 0.989

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PRICE Vs. Wind Correlation Coefficient = 0.9204

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PRICE Vs. PWRREC Correlation Coefficient = 0.9779

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DCAA EZ Quant Application

The Equation Fitted by Regression Y = a + bX

Coefficients Estimated by Regression:

Variable Coefficient Value

Computed T-Value

Inclusion Assurance

(%) a Intercept 1.01085745056 1.8263 90.9 b X1 is SqFt 0.0233965137238 24.1164 99.9

Summary Statistics:

R squared Adjusted R squared

Regression Std Error

Dep Var Std Deviation

0.978137 0.976455 0.9329 6.0797

Comparison Assurance

(%) F Statistic Regression Deg Frdm

Residual Deg Frdm

99.9 581.5987 1 13

Data Item Actual Value Calculated Value

Difference (Actual-Calc'd)

% Diff (Diff/Actual)

1 3.7500 2.9996 0.7504 20.0 2 4.4550 4.9883 -0.5333 -12.0 3 5.8500 5.6902 0.1598 2.7 4 6.9990 7.9128 -0.9138 -13.1 5 7.4490 9.3166 -1.8676 -25.1 6 11.3250 10.7204 0.6046 5.3 7 12.9500 11.5393 1.4107 10.9 8 13.7800 12.9431 0.8369 6.1 9 14.2500 15.0488 -0.7988 -5.6

10 16.9850 16.2186 0.7664 4.5 11 17.8200 18.4413 -0.6213 -3.5 12 18.9650 17.9733 0.9917 5.2 13 19.4500 19.4941 -0.0441 -0.2 14 19.9850 20.6639 -0.6789 -3.4 15 21.4200 21.4828 -0.0628 -0.3

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Minitab Regression Analysis: Price versus SqFt The regression equation is Price = 1.01 + 0.0234 SqFt Predictor Coef SE Coef T P Constant 1.0109 0.5535 1.83 0.091 SqFt 0.0233965 0.0009702 24.12 0.000 S = 0.932897 R-Sq = 97.8% R-Sq(adj) = 97.6% Analysis of Variance Source DF SS MS F P Regression 1 506.16 506.16 581.60 0.000 Residual Error 13 11.31 0.87 Total 14 517.48 Obs SqFt Price Fit SE Fit Residual St Resid 1 85 3.750 3.000 0.481 0.750 0.94 2 170 4.455 4.988 0.411 -0.533 -0.64 3 200 5.850 5.690 0.388 0.160 0.19 4 295 6.999 7.913 0.321 -0.914 -1.04 5 355 7.449 9.317 0.286 -1.868 -2.10R 6 415 11.325 10.720 0.259 0.605 0.67 7 450 12.950 11.539 0.249 1.411 1.57 8 510 13.780 12.943 0.241 0.837 0.93 9 600 14.250 15.049 0.255 -0.799 -0.89 10 650 16.985 16.219 0.275 0.766 0.86 11 745 17.820 18.441 0.329 -0.621 -0.71 12 725 18.965 17.973 0.316 0.992 1.13 13 790 19.450 19.494 0.360 -0.044 -0.05 14 840 19.985 20.664 0.398 -0.679 -0.80 15 875 21.420 21.483 0.425 -0.063 -0.08 R denotes an observation with a large standardized residual.

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Personnel Shelters Regression Output in Excel

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.989007843R Square 0.978136514Adjusted R Square 0.976454708Standard Error 0.932896799Observations 15

ANOVAdf SS MS F Significance F

Regression 1 506.163274 506.163274 581.5986966 3.53961E-12Residual 13 11.31385369 0.870296438Total 14 517.4771277

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 1.010857451 0.553495152 1.826316721 0.090848172 -0.184896127 2.206611028SQFT 0.023396514 0.000970151 24.11635745 3.53961E-12 0.021300629 0.025492398

RESIDUAL OUTPUT

Observation Predicted PRICE Residuals Standard Residuals1 2.999561117 0.750438883 0.8347839052 4.988264784 -0.533264784 -0.5932006843 5.690160195 0.159839805 0.1778048814 7.912828999 -0.913828999 -1.0165381325 9.316619822 -1.867619822 -2.0775295686 10.72041065 0.604589354 0.6725417267 11.53928863 1.410711374 1.5692672338 12.94307945 0.83692055 0.9309856159 15.04876568 -0.798765685 -0.888542362

10 16.21859137 0.766408629 0.85254855911 18.44126017 -0.621260175 -0.69108625112 17.9733299 0.9916701 1.10312812513 19.49410329 -0.044103292 -0.04906024914 20.66392898 -0.678928979 -0.75523669815 21.48280696 -0.062806959 -0.0698661

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SQFT Residual Plot

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SQFT Line Fit Plot

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Linear Analysis for Dataset Shelters

I. Model Form and Equation Table

Model Form: Unweighted Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = 1.011 + 0.0234 * SQFT

II. Fit Measures (in Fit Space)

Coefficient Statistics Summary

Variable Coefficient Std Dev of Coef Beta ValueT-Statistic (Coef/SD) Prob Not Zero

Intercept 1.0109 0.5535 1.8263 0.9093SQFT 0.0234 0.0010 0.9890 24.1164 1.0000

Goodness-of-Fit Statistics

Std Error (SE) R-Squared R-Squared (Adj)Pearson's Corr Coef

0.9329 97.81% 97.65% 0.9890

Analysis of Variance

Due To DF Sum of Sqr (SS)Mean SQ =

SS/DF F-Stat Prob Not ZeroRegression 1 506.1633 506.1633 581.5987 1.0000Residual (Error) 13 11.3139 0.8703Total 14 517.4771

Outlier Analysis Summary

Observations exhibiting unusual values Dependent Variable #5Independent Variable Observations influencing coefficients

Outlier Analysis Table

Obs # PRICEPredicted Y

Value ResidualStd. Dev. Pred Y Std. Residual Leverage

Cook's Distance Flags

1 3.7500 2.9996 0.7504 0.4806 0.9385 0.2654 0.15912 4.4550 4.9883 -0.5333 0.4113 -0.6369 0.1944 0.04893 5.8500 5.6902 0.1598 0.3881 0.1884 0.1731 0.00374 6.9990 7.9128 -0.9138 0.3210 -1.0433 0.1184 0.07315 7.4490 9.3166 -1.8676 0.2859 -2.1031 0.0939 0.2292 R6 11.3250 10.7204 0.6046 0.2592 0.6746 0.0772 0.01907 12.9500 11.5393 1.4107 0.2487 1.5689 0.0711 0.09418 13.7800 12.9431 0.8369 0.2409 0.9286 0.0667 0.03089 14.2500 15.0488 -0.7988 0.2550 -0.8901 0.0747 0.032010 16.9850 16.2186 0.7664 0.2748 0.8597 0.0868 0.035111 17.8200 18.4413 -0.6213 0.3292 -0.7117 0.1245 0.036012 18.9650 17.9733 0.9917 0.3163 1.1299 0.1150 0.082913 19.4500 19.4941 -0.0441 0.3604 -0.0513 0.1492 0.000214 19.9850 20.6639 -0.6789 0.3978 -0.8046 0.1818 0.071915 21.4200 21.4828 -0.0628 0.4253 -0.0756 0.2079 0.0008

SE = 0.9329, Mean = 13.0289, Coef. of Var. = 7.16% in Fit SpaceR denotes an observation with an unusual Dependent variable value.

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III. Predictive Measures (in Unit Space)

Percentage Error Table

Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 2.9996 0.7504 -20.01172 4.4550 4.9883 -0.5333 11.97003 5.8500 5.6902 0.1598 -2.73234 6.9990 7.9128 -0.9138 13.05665 7.4490 9.3166 -1.8676 25.0721 @6 11.3250 10.7204 0.6046 -5.33857 12.9500 11.5393 1.4107 -10.89358 13.7800 12.9431 0.8369 -6.07349 14.2500 15.0488 -0.7988 5.605410 16.9850 16.2186 0.7664 -4.512311 17.8200 18.4413 -0.6213 3.486312 18.9650 17.9733 0.9917 -5.228913 19.4500 19.4941 -0.0441 0.226814 19.9850 20.6639 -0.6789 3.397215 21.4200 21.4828 -0.0628 0.2932

Avg (Arith) 13.0289 13.0289 0.0000 0.55%Avg (Absolute) 0.7361 7.86%

@ Refer to outlier analysis table.

Summary of Predictive Measures

Average Actual (Avg Act) 13.0289Standard Error (SE) 0.9329Root Mean Square (RMS) of % Errors 10.45%Mean Absolute Deviation (Mad) of % Errors 7.86%Coef of Variation based on Std Error (SE/Avg Act) 7.16%Coef of Variation based on MAD Res (MAD Res/Avg Act) 5.65%Pearson's Correlation Coefficient between Act & Pred 98.90%Adjusted R-Squared in Unit Space 97.65%

IV. Prediction IntervalsNot available or inappropriately defined. Inputs must be specified for all independent and dummyvariables.

V. Charts

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Actual vs. Predicted (Unit Space)

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Standardized Residual (Fit Space)

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Equation vs. Variable (Unit Space)

Actual Predicted

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Model Form: Unweighted Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = 3.838 + 0.6127 * PWRREC



Variable Coefficient Std Dev of Coef Beta ValueT-Statistic (Coef/SD) P-Value

Prob Not Zero

Intercept 3.8379 0.6431 5.9675 0.0000 1.0000PWRREC 0.6127 0.0364 0.9779 16.8516 0.0000 1.0000


Std Error (SE) R-SquaredR-Squared

(Adj)Pearson's Corr Coef

1.3200 95.62% 95.29% 0.9779


Due To DFSum of Sqr

(SS)Mean SQ =

SS/DF F-Stat P-ValueProb Not

ZeroRegression 1 494.8247 494.8247 283.9753 0.0000 1.0000Residual (Error) 13 22.6524 1.7425Total 14 517.4771



Value ResidualStd. Dev.

Pred Y Std. Residual LeverageCook's

Distance Flags1 3.7500 5.0634 -1.3134 0.5827 -1.1089 0.1949 0.14882 4.4550 5.6761 -1.2211 0.5537 -1.0190 0.1759 0.11083 5.8500 6.2889 -0.4389 0.5255 -0.3624 0.1585 0.01244 6.9990 7.5143 -0.5153 0.4725 -0.4181 0.1281 0.01285 7.4490 8.7398 -1.2908 0.4254 -1.0329 0.1038 0.06186 11.3250 9.9652 1.3598 0.3863 1.0773 0.0856 0.05437 12.9500 11.1907 1.7593 0.3579 1.3846 0.0735 0.07608 13.7800 12.4161 1.3639 0.3428 1.0699 0.0674 0.04149 14.2500 13.6416 0.6084 0.3428 0.4773 0.0674 0.0082

10 16.9850 14.8671 2.1179 0.3579 1.6669 0.0735 0.110211 17.8200 17.3180 0.5020 0.4254 0.4018 0.1038 0.009412 18.9650 18.5434 0.4216 0.4725 0.3420 0.1281 0.008613 19.4500 19.7689 -0.3189 0.5255 -0.2633 0.1585 0.006514 19.9850 20.9943 -1.0093 0.5827 -0.8522 0.1949 0.087915 21.4200 23.4452 -2.0252 0.7059 -1.8156 0.2859 0.6600

SE = 1.3200, Mean = 13.0289, Coef. of Var. = 10.13% in Fit Space

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Influential Observation All 15 observations PRICE = 3.838 + 0.6127 * Rec Without obs.…

1 PRICE = 4.188 + 0.5966 * Rec 2 PRICE = 4.139 + 0.5992 * Rec 3 PRICE = 3.938 + 0.6084 * Rec 4 PRICE = 3.938 + 0.6087 * Rec 5 PRICE = 4.049 + 0.6051 * Rec : : : : : : : :

15 PRICE = 3.478 + 0.6493 * Rec



Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 5.0634 -1.3134 35.02392 4.4550 5.6761 -1.2211 27.41023 5.8500 6.2889 -0.4389 7.50184 6.9990 7.5143 -0.5153 7.36265 7.4490 8.7398 -1.2908 17.32816 11.3250 9.9652 1.3598 -12.00687 12.9500 11.1907 1.7593 -13.58558 13.7800 12.4161 1.3639 -9.89749 14.2500 13.6416 0.6084 -4.2695

10 16.9850 14.8671 2.1179 -12.469511 17.8200 17.3180 0.5020 -2.817312 18.9650 18.5434 0.4216 -2.222913 19.4500 19.7689 -0.3189 1.639514 19.9850 20.9943 -1.0093 5.050515 21.4200 23.4452 -2.0252 9.4549Avg (Arith) 13.0289 13.0289 0.0000 3.57%Avg (Absolute) 1.0844 11.20%



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Model Form: Unweighted Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = (-9.528) + 0.5088 * Rating




Intercept -9.5282 2.7328 -3.4866 0.9960Rating 0.5088 0.0599 0.9204 8.4880 1.0000



2.4667 84.71% 83.54% 0.9204





Observations exhibiting unusual values Dependent Variable Independent Variable #1Observations influencing coefficients #1



Value ResidualStd. Dev. Pred Y Std. Residual Leverage

Cook's Distance Flags

1 3.7500 0.6479 3.1021 1.5916 1.6461 0.4163 0.9664 XD2 4.4550 3.1919 1.2631 1.3224 0.6066 0.2874 0.07423 5.8500 8.2800 -2.4300 0.8477 -1.0490 0.1181 0.07374 6.9990 8.2800 -1.2810 0.8477 -0.5530 0.1181 0.02055 7.4490 10.8240 -3.3750 0.6878 -1.4247 0.0778 0.08566 11.3250 13.3681 -2.0431 0.6382 -0.8574 0.0669 0.02647 12.9500 15.9121 -2.9621 0.7218 -1.2558 0.0856 0.07388 13.7800 15.9121 -2.1321 0.7218 -0.9039 0.0856 0.03839 14.2500 15.9121 -1.6621 0.7218 -0.7047 0.0856 0.023310 16.9850 15.9121 1.0729 0.7218 0.4549 0.0856 0.009711 17.8200 15.9121 1.9079 0.7218 0.8089 0.0856 0.030612 18.9650 18.4561 0.5089 0.9025 0.2217 0.1339 0.003813 19.4500 15.9121 3.5379 0.7218 1.4999 0.0856 0.105314 19.9850 18.4561 1.5289 0.9025 0.6660 0.1339 0.034315 21.4200 18.4561 2.9639 0.9025 1.2911 0.1339 0.1288

SE = 2.4667, Mean = 13.0289, Coef. of Var. = 18.93% in Fit SpaceX denotes an observation with an unusual Independent variable value.D denotes an observation with an unusual influence on the fitted regression equation.

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Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 0.6479 3.1021 -82.7223 @2 4.4550 3.1919 1.2631 -28.35143 5.8500 8.2800 -2.4300 41.53864 6.9990 8.2800 -1.2810 18.30275 7.4490 10.8240 -3.3750 45.30866 11.3250 13.3681 -2.0431 18.04047 12.9500 15.9121 -2.9621 22.87348 13.7800 15.9121 -2.1321 15.47249 14.2500 15.9121 -1.6621 11.663910 16.9850 15.9121 1.0729 -6.316711 17.8200 15.9121 1.9079 -10.706512 18.9650 18.4561 0.5089 -2.683213 19.4500 15.9121 3.5379 -18.189714 19.9850 18.4561 1.5289 -7.650115 21.4200 18.4561 2.9639 -13.8369





IV. Prediction IntervalsNot available or inappropriately defined. Inputs must be specified for all independent and dummyvariables.

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Model Form: Unweighted Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = (-3.189) + 4.45 * SqrtRec




Intercept -3.1888 0.5979 -5.3330 0.9999SqrtRec 4.4496 0.1544 0.9923 28.8210 1.0000



0.7832 98.46% 98.34% 0.9923










Distance Flags1 3.7500 3.1038 0.6462 0.3994 0.9591 0.2600 0.16162 4.4550 4.5181 -0.0631 0.3579 -0.0905 0.2088 0.00113 5.8500 5.7103 0.1397 0.3246 0.1960 0.1718 0.00404 6.9990 7.7104 -0.7114 0.2738 -0.9694 0.1222 0.06545 7.4490 9.3965 -1.9475 0.2383 -2.6103 0.0926 0.3475 R6 11.3250 10.8820 0.4430 0.2155 0.5884 0.0757 0.01427 12.9500 12.2249 0.7251 0.2041 0.9589 0.0679 0.03358 13.7800 13.4600 0.3200 0.2028 0.4231 0.0670 0.00649 14.2500 14.6095 -0.3595 0.2095 -0.4763 0.0716 0.008710 16.9850 15.6891 1.2959 0.2223 1.7256 0.0806 0.130411 17.8200 17.6815 0.1385 0.2588 0.1873 0.1092 0.002212 18.9650 18.6095 0.3555 0.2800 0.4860 0.1278 0.017313 19.4500 19.4996 -0.0496 0.3022 -0.0687 0.1488 0.000414 19.9850 20.3561 -0.3711 0.3248 -0.5207 0.1720 0.028215 21.4200 21.9818 -0.5618 0.3707 -0.8142 0.2240 0.0957


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SqrtRec


Actual Predicted



Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 3.1038 0.6462 -17.23142 4.4550 4.5181 -0.0631 1.41553 5.8500 5.7103 0.1397 -2.38774 6.9990 7.7104 -0.7114 10.16375 7.4490 9.3965 -1.9475 26.1440 @6 11.3250 10.8820 0.4430 -3.91217 12.9500 12.2249 0.7251 -5.59898 13.7800 13.4600 0.3200 -2.32269 14.2500 14.6095 -0.3595 2.522610 16.9850 15.6891 1.2959 -7.629611 17.8200 17.6815 0.1385 -0.777112 18.9650 18.6095 0.3555 -1.874313 19.4500 19.4996 -0.0496 0.255214 19.9850 20.3561 -0.3711 1.856915 21.4200 21.9818 -0.5618 2.6226





30

Power Model (Log-Log Transformation in Excel on # Receptacles and Price)

Rec Price LN (Rec) LN (Price) SUMMARY OUTPUT2 3.750 0.6931 1.32183 4.455 1.0986 1.4940 Regression Statistics4 5.850 1.3863 1.7664 Multiple R 0.9917461856 6.999 1.7918 1.9458 R Square 0.9835604968 7.449 2.0794 2.0081 Adjusted R Square 0.982295919

10 11.325 2.3026 2.4270 Standard Error 0.07710387212 12.950 2.4849 2.5611 Observations 1514 13.780 2.6391 2.623216 14.250 2.7726 2.6568 ANOVA18 16.985 2.8904 2.8323 df SS MS F22 17.820 3.0910 2.8803 Regression 1 4.623896473 4.623896473 777.77812524 18.965 3.1781 2.9426 Residual 13 0.077285092 0.00594500726 19.450 3.2581 2.9678 Total 14 4.70118156528 19.985 3.3322 2.995032 21.420 3.4657 3.0643 Coefficients Standard Error t Stat P-value

Intercept 0.809190275 0.061515037 13.15434913 6.89351E-09LN (Rec) 0.667748213 0.023943348 27.88867378 5.53373E-13

Intercept (Unit Space) 2.246088537Exponent for Pwr Rec 0.667748213

Equation in Unit Space: Price = (2.2461)(Pwr Rec)^.667748

Actual Predicted Residual Residual^23.750 3.568 0.182 0.033 SSE 10.0774.455 4.678 -0.223 0.0505.850 5.668 0.182 0.033 DF 136.999 7.431 -0.432 0.1867.449 9.005 -1.556 2.420 Variance (MSE) 0.775

11.325 10.451 0.874 0.76312.950 11.805 1.145 1.312 SE 0.88013.780 13.084 0.696 0.48414.250 14.305 -0.055 0.003 Mean 13.02916.985 15.475 1.510 2.28017.820 17.694 0.126 0.016 CV 6.76%18.965 18.753 0.212 0.04519.450 19.782 -0.332 0.11019.985 20.786 -0.801 0.64121.420 22.724 -1.304 1.701

10.077

Cost Mean Residual Residual^23.750 13.029 -9.279 86.097 SST 517.4774.455 13.029 -8.574 73.5115.850 13.029 -7.179 51.536 R^2 98.05%6.999 13.029 -6.030 36.3597.449 13.029 -5.580 31.135 n-1 14

11.325 13.029 -1.704 2.90312.950 13.029 -0.079 0.006 n-p 1313.780 13.029 0.751 0.56414.250 13.029 1.221 1.491 R^2 adjusted 97.90%16.985 13.029 3.956 15.65117.820 13.029 4.791 22.95518.965 13.029 5.936 35.23819.450 13.029 6.421 41.23119.985 13.029 6.956 48.38821.420 13.029 8.391 70.411

517.477

31

Log Linear Analysis for Dataset Shelters


Model Form: Unweighted Log-Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = 2.246 * Rec ^ 0.6677




Intercept 0.8092 0.0615 13.1543 1.0000Rec 0.6677 0.0239 0.9917 27.8887 1.0000



0.0771 98.36% 98.23% 0.9917







Obs # Log of PRICEPredicted Y



Distance Flags1 1.3218 1.2720 0.0497 0.0461 0.8047 0.3579 0.18042 1.4940 1.5428 -0.0488 0.0376 -0.7244 0.2378 0.08193 1.7664 1.7349 0.0316 0.0320 0.4497 0.1719 0.02104 1.9458 2.0056 -0.0599 0.0251 -0.8212 0.1061 0.04005 2.0081 2.1977 -0.1897 0.0216 -2.5625 0.0786 0.2800 R6 2.4270 2.3467 0.0803 0.0201 1.0786 0.0683 0.04267 2.5611 2.4685 0.0926 0.0200 1.2435 0.0669 0.05558 2.6232 2.5714 0.0518 0.0205 0.6970 0.0708 0.01859 2.6568 2.6606 -0.0038 0.0215 -0.0517 0.0779 0.000110 2.8323 2.7392 0.0931 0.0227 1.2637 0.0870 0.076111 2.8803 2.8732 0.0071 0.0254 0.0974 0.1087 0.000612 2.9426 2.9313 0.0113 0.0268 0.1558 0.1205 0.001713 2.9678 2.9848 -0.0169 0.0281 -0.2358 0.1326 0.004314 2.9950 3.0343 -0.0393 0.0294 -0.5510 0.1450 0.025715 3.0643 3.1234 -0.0591 0.0318 -0.8414 0.1699 0.0725


32

0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-3.0000

-2.5000

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

0.0000 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000 4.0000

Std.

Res

idua

l

Rec


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000 35.0000

Pred

icte

d (P

RICE

)

Rec


Actual Predicted



Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 3.5681 0.1819 -4.85022 4.4550 4.6776 -0.2226 4.99683 5.8500 5.6683 0.1817 -3.10634 6.9990 7.4308 -0.4318 6.16965 7.4490 9.0046 -1.5556 20.8831 @6 11.3250 10.4514 0.8736 -7.71387 12.9500 11.8045 1.1455 -8.84548 13.7800 13.0843 0.6957 -5.04839 14.2500 14.3046 -0.0546 0.383210 16.9850 15.4751 1.5099 -8.889711 17.8200 17.6940 0.1260 -0.706812 18.9650 18.7526 0.2124 -1.120213 19.4500 19.7821 -0.3321 1.707514 19.9850 20.7857 -0.8007 4.006415 21.4200 22.7242 -1.3042 6.0886





33

Log Linear Analysis for Dataset Shelters


Model Form: Unweighted Log-Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = 0.01264 * Rating ^ 1.812




Intercept -4.3710 0.6279 -6.9613 1.0000Rating 1.8122 0.1667 0.9491 10.8682 1.0000



0.1894 90.09% 89.32% 0.9491





Observations exhibiting unusual values Dependent Variable Independent Variable #1Observations influencing coefficients #1


Obs # Log of PRICEPredicted Y



Distance Flags1 1.3218 1.0577 0.2640 0.1356 1.9979 0.5129 2.1015 XD2 1.4940 1.4621 0.0319 0.1018 0.2000 0.2890 0.00813 1.7664 2.0718 -0.3054 0.0591 -1.6976 0.0974 0.15544 1.9458 2.0718 -0.1261 0.0591 -0.7008 0.0974 0.02655 2.0081 2.3138 -0.3057 0.0501 -1.6743 0.0700 0.10556 2.4270 2.5273 -0.1003 0.0497 -0.5487 0.0688 0.01117 2.5611 2.7182 -0.1571 0.0555 -0.8678 0.0859 0.03548 2.6232 2.7182 -0.0950 0.0555 -0.5247 0.0859 0.01299 2.6568 2.7182 -0.0614 0.0555 -0.3394 0.0859 0.005410 2.8323 2.7182 0.1141 0.0555 0.6304 0.0859 0.018711 2.8803 2.7182 0.1621 0.0555 0.8955 0.0859 0.037712 2.9426 2.8909 0.0517 0.0646 0.2903 0.1163 0.005513 2.9678 2.7182 0.2496 0.0555 1.3790 0.0859 0.089414 2.9950 2.8909 0.1041 0.0646 0.5846 0.1163 0.022515 3.0643 2.8909 0.1734 0.0646 0.9742 0.1163 0.0625

SE = 0.1894, Mean = 2.4324, Coef. of Var. = 7.78% in Fit SpaceX denotes an observation with an unusual Independent variable value.D denotes an observation with an unusual influence on the fitted regression equation.

34



Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 2.8798 0.8702 -23.2049 @2 4.4550 4.3150 0.1400 -3.14233 5.8500 7.9395 -2.0895 35.71714 6.9990 7.9395 -0.9405 13.43705 7.4490 10.1130 -2.6640 35.76376 11.3250 12.5193 -1.1943 10.54537 12.9500 15.1530 -2.2030 17.01178 13.7800 15.1530 -1.3730 9.96389 14.2500 15.1530 -0.9030 6.336910 16.9850 15.1530 1.8320 -10.785911 17.8200 15.1530 2.6670 -14.966312 18.9650 18.0098 0.9552 -5.036513 19.4500 15.1530 4.2970 -22.092514 19.9850 18.0098 1.9752 -9.883315 21.4200 18.0098 3.4102 -15.9205





0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

0.0000 1.0000 2.0000 3.0000 4.0000 5.0000

Std.

Res

idua

l

Rating


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 10.0000 20.0000 30.0000 40.0000 50.0000 60.0000

Pred

icte

d (P

RICE

)

Rating


Actual Predicted

35



Model Form: Unweighted Linear modelNumber of Observations Used: 15Equation in Unit Space: PRICE = 12.31 + (-0.7338) * Rating + 0.016 * RatingSq




Intercept 12.3141 6.5916 1.8682 0.9138Rating -0.7338 0.3599 -1.3275 -2.0390 0.9360RatingSq 0.0160 0.0046 2.2649 3.4788 0.9955



1.8116 92.39% 91.12% 0.9612




Further Analysis of Variance

(SS explained by each variable when entered in the order given)

Due To DF Sequential SSRegression 2 478.0948Rating 1 438.3767RatingSq 1 39.7182

Pairwise Correlation Matrix

Variables PRICE Rating RatingSqPRICE 1.0000 0.9204 0.9474Rating 0.9204 1.0000 0.9925RatingSq 0.9474 0.9925 1.0000

Multicollinearity Analysis

Indep Variables Indiv R-Sqr (%) F-StatsProb Related to Other Vars

Indiv R-Sqr/Model R-

Sqr FlagsRating 98.50% 855.8342 1.0000 1.0662 XRatingSq 98.50% 855.8342 1.0000 1.0662 X

X = The indicated independent variable could be harmfully correlated to the other independentvariables, i.e., it has a nearly better fit using the remaining independent variables than thedependent variable.

36


Observations exhibiting unusual values Dependent Variable #13Independent Variable #1Observations influencing coefficients





Distance Flags1 3.7500 4.0364 -0.2864 1.5215 -0.2912 0.7054 0.0677 X2 4.4550 3.9666 0.4884 0.9964 0.3228 0.3025 0.01513 5.8500 6.2268 -0.3768 0.8579 -0.2361 0.2243 0.00544 6.9990 6.2268 0.7722 0.8579 0.4840 0.2243 0.02265 7.4490 8.5567 -1.1077 0.8246 -0.6867 0.2072 0.04116 11.3250 11.6864 -0.3614 0.6733 -0.2149 0.1381 0.00257 12.9500 15.6161 -2.6661 0.5369 -1.5409 0.0878 0.07628 13.7800 15.6161 -1.8361 0.5369 -1.0612 0.0878 0.03619 14.2500 15.6161 -1.3661 0.5369 -0.7896 0.0878 0.020010 16.9850 15.6161 1.3689 0.5369 0.7912 0.0878 0.020111 17.8200 15.6161 2.2039 0.5369 1.2738 0.0878 0.052112 18.9650 20.3456 -1.3806 0.8569 -0.8650 0.2237 0.071913 19.4500 15.6161 3.8339 0.5369 2.2159 0.0878 0.1576 R14 19.9850 20.3456 -0.3606 0.8569 -0.2259 0.2237 0.004915 21.4200 20.3456 1.0744 0.8569 0.6731 0.2237 0.0435

SE = 1.8116, Mean = 13.0289, Coef. of Var. = 13.90% in Fit SpaceR denotes an observation with an unusual Dependent variable value.X denotes an observation with an unusual Independent variable value.



Obs # Name Actuals Predicted Residuals % Errors Flags1 3.7500 4.0364 -0.2864 7.6365 @2 4.4550 3.9666 0.4884 -10.96233 5.8500 6.2268 -0.3768 6.44074 6.9990 6.2268 0.7722 -11.03335 7.4490 8.5567 -1.1077 14.87016 11.3250 11.6864 -0.3614 3.19157 12.9500 15.6161 -2.6661 20.58758 13.7800 15.6161 -1.8361 13.32439 14.2500 15.6161 -1.3661 9.586610 16.9850 15.6161 1.3689 -8.059611 17.8200 15.6161 2.2039 -12.367612 18.9650 20.3456 -1.3806 7.279813 19.4500 15.6161 3.8339 -19.7116 @14 19.9850 20.3456 -0.3606 1.804415 21.4200 20.3456 1.0744 -5.0158



37



0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

0.0000 10.0000 20.0000 30.0000 40.0000 50.0000 60.0000

Std.

Res

idua

l

Rating


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 10.0000 20.0000 30.0000 40.0000 50.0000 60.0000

Pred

icte

d (P

RICE

)

Rating


Actual Predicted

39

Depot Reparables (regression)

You’re monitoring a contract for depot reparables and you notice a significant variation in the time from when a part is pulled for repair in the field and

the time that the part actually arrives at the contractor’s repair facility. You speculate that the weight of the part may contribute to the delivery time and you have collected some data to test your theory.

DAYS WT

62 12 13 23 12 32 56 36 14 39 55 45 8 48

78 52 44 57 40 65 90 69 22 73 61 77 33 82 6 87

111 51 55 95 33 99

40

Univariate Analysis for Dataset Delivery Data

I. Univariate Data Analysis

Data Description

Variable DAYS# of Observations 18# of Missing Values 0Maximum 111.0000Minimum 6.0000Range 105.0000

Descriptive Measures

Average 44.0556Std. Dev. (Sample) 29.8299RMS (Population) 28.9894Median 42.00001st Quartile 13.75003rd Quartile 61.2500Skewness 0.5442

Confidence Interval (Predicting Population Mean from Sample Mean)

Sample Mean 44.0556Lower Bound 29.2202Upper Bound 58.8909Std Error 7.0310Confidence Level 95.00%

Prediction Interval (Predicting Individual Observation from Sample Mean)

Lower Bound -20.6102Upper Bound 108.7213Confidence Level 95.00%

II. Histogram

0

1

2

3

4

5

6

7

16.50 37.51 58.51 79.51 100.5

Coun

t

Bin Centers

Univariate Histogram

41

Linear Analysis for Dataset Delivery Data


Model Form: Unweighted Linear modelNumber of Observations Used: 18Equation in Unit Space: DAYS = 44.05 + 1.0643e-005 * WT




Intercept 44.0549 18.8681 2.3349 0.9671WT 0.0000 0.3009 0.0000 0.0000 0.0000



30.7479 0.00% -6.25% 0.0000







Obs # DAYSPredicted Y



Distance Flags1 62.0000 44.0551 17.9449 15.5957 0.6772 0.2573 0.07942 13.0000 44.0552 -31.0552 12.7577 -1.1101 0.1722 0.12813 12.0000 44.0553 -32.0553 10.6405 -1.1112 0.1198 0.08404 56.0000 44.0553 11.9447 9.7936 0.4098 0.1014 0.00955 14.0000 44.0554 -30.0554 9.2106 -1.0245 0.0897 0.05176 55.0000 44.0554 10.9446 8.2200 0.3694 0.0715 0.00537 8.0000 44.0555 -36.0555 7.8345 -1.2126 0.0649 0.05108 78.0000 44.0555 33.9445 7.4609 1.1380 0.0589 0.04059 44.0000 44.0555 -0.0555 7.2523 -0.0019 0.0556 0.000010 40.0000 44.0556 -4.0556 7.5567 -0.1361 0.0604 0.000611 90.0000 44.0557 45.9443 7.9815 1.5473 0.0674 0.086512 22.0000 44.0557 -22.0557 8.5559 -0.7468 0.0774 0.023413 61.0000 44.0558 16.9442 9.2520 0.5778 0.0905 0.016614 33.0000 44.0558 -11.0558 10.2553 -0.3814 0.1112 0.009115 6.0000 44.0559 -38.0559 11.3697 -1.3321 0.1367 0.140516 111.0000 44.0555 66.9445 7.5380 2.2457 0.0601 0.1612 R17 55.0000 44.0560 10.9440 13.3134 0.3949 0.1875 0.018018 33.0000 44.0560 -11.0560 14.3382 -0.4065 0.2174 0.0230


42



Obs # Name Actuals Predicted Residuals % Errors Flags1 62.0000 44.0551 17.9449 -28.94342 13.0000 44.0552 -31.0552 238.88603 12.0000 44.0553 -32.0553 267.12734 56.0000 44.0553 11.9447 -21.32985 14.0000 44.0554 -30.0554 214.68116 55.0000 44.0554 10.9446 -19.89927 8.0000 44.0555 -36.0555 450.69318 78.0000 44.0555 33.9445 -43.51869 44.0000 44.0555 -0.0555 0.126210 40.0000 44.0556 -4.0556 10.139111 90.0000 44.0557 45.9443 -51.049312 22.0000 44.0557 -22.0557 100.253313 61.0000 44.0558 16.9442 -27.777414 33.0000 44.0558 -11.0558 33.502515 6.0000 44.0559 -38.0559 634.264416 111.0000 44.0555 66.9445 -60.3104 @17 55.0000 44.0560 10.9440 -19.898318 33.0000 44.0560 -11.0560 33.5030




Average Actual (Avg Act) 44.0556Standard Error (SE) 30.7479Root Mean Square (RMS) of % Errors 211.26%Mean Absolute Deviation (Mad) of % Errors 125.33%Coef of Variation based on Std Error (SE/Avg Act) 69.79%Coef of Variation based on MAD Res (MAD Res/Avg Act) 54.36%Pearson's Correlation Coefficient between Act & Pred 0.00%Adjusted R-Squared in Unit Space -6.25%

43

0.0000

20.0000

40.0000

60.0000

80.0000

100.0000

120.0000

0.0000 20.0000 40.0000 60.0000 80.0000 100.0000 120.0000

Pred

icte

d (D

AYS)

Actual


-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

0.0000 20.0000 40.0000 60.0000 80.0000 100.0000 120.0000

Std.

Res

idua

l

WT


0.0000

20.0000

40.0000

60.0000

80.0000

100.0000

120.0000

0.0000 20.0000 40.0000 60.0000 80.0000 100.0000 120.0000

Pred

icte

d (D

AYS)

WT


Actual Predicted

45

Airborne Radios (regression)

Your pricing shop has responsibility for the procurement of various electronic systems, including radios. Your supervisor has been told to expect a number of requirements for the purchase of various types of airborne radios due to upcoming fleet-wide upgrades. You have been tasked to perform some preliminary price analysis to assist your office in determining fair and reasonable prices for a certain range of radio sets. Your first step was to perform market research and collect price, technical, and background data for airborne radio communication sets. Following your technical experts’ advice, you collected data on weight, power output, and frequency. Each had been identified as a significant contributor to the price of an airborne radio set. You then normalized for quantity and adjusted the prices to CY09$K. The experts suggested the following relationships between price and the technical parameters: Weight - It is expected that price will increase with increased radio weight, given that no miniaturization is planned. Power Output - It is expected that price will increase with increased power output. Frequency - Increases in frequencies increase communication capacity. For a given power output, higher frequency radio sets are more expensive. The table below contains the data for ten airborne radios.

OBS.

SYSTEM

PRICE (CY09K)

WEIGHT (LBS)

PWR OUT (WATTS)

FREQ (MHZ)

1 Motorola 200X 22.2 90 20 400 2 Sanders 212 17.3 161 400 30 3 Motorola GYX 11.8 40 30 400 4 Sony 12B 9.6 108 10 400 5 Westinghouse 9X 8.8 82 10 400 6 Loral 139SX 7.6 135 100 25 7 Loral 125XT 6.8 59 6 400 8 Motorola 190 3.2 68 8 156 9 Sundstrand 500 1.7 25 8 42

10 Sundstrand 40SL 1.6 24 0.5 258

46

0

1

2

3

4

5

6

4.175 9.326 14.48 19.63

Coun

t

Bin Centers

Univariate Histogram

Univariate Analysis for Dataset Radios

I. Univariate Data Analysis

Data Description

Variable PRICE# of Observations 10# of Missing Values 0Maximum 22.2000Minimum 1.6000Range 20.6000

Descriptive Measures

Average 9.0600Std. Dev. (Sample) 6.6652RMS (Population) 6.3232Median 8.20001st Quartile 2.82503rd Quartile 13.1750Skewness 0.6827

Confidence Interval (Predicting Population Mean from Sample Mean)

Sample Mean 9.0600Lower Bound 4.2923Upper Bound 13.8277Std Error 2.1077Confidence Level 95.00%

II. Histogram

47

Linear Analysis for Dataset Radios


Model Form: Unweighted Linear modelNumber of Observations Used: 10Equation in Unit Space: PRICE = 2.477 + 0.08312 * WT




Intercept 2.4765 3.8234 0.6477 0.4647WT 0.0831 0.0423 0.5702 1.9632 0.9148



5.8076 32.51% 24.08% 0.5702










Distance Flags1 22.2000 9.9577 12.2423 1.8926 2.2297 0.1062 0.2954 R2 17.3000 15.8596 1.4404 3.9203 0.3362 0.4557 0.04733 11.8000 5.8015 5.9985 2.4754 1.1418 0.1817 0.14474 9.6000 11.4540 -1.8540 2.2045 -0.3451 0.1441 0.01005 8.8000 9.2927 -0.4927 1.8403 -0.0895 0.1004 0.00046 7.6000 13.6984 -6.0984 2.9925 -1.2252 0.2655 0.27137 6.8000 7.3809 -0.5809 2.0259 -0.1067 0.1217 0.00088 3.2000 8.1290 -4.9290 1.8968 -0.8980 0.1067 0.04819 1.7000 4.5546 -2.8546 2.9393 -0.5699 0.2561 0.055910 1.6000 4.4715 -2.8715 2.9724 -0.5755 0.2620 0.0588


48



Obs # Name Actuals Predicted Residuals % Errors Flags1 Motorola 200X 22.2000 9.9577 12.2423 -55.1453 @2 Sanders 212 17.3000 15.8596 1.4404 -8.32593 Motorola GYX 11.8000 5.8015 5.9985 -50.83474 Sony 12B 9.6000 11.4540 -1.8540 19.31255 Westinghouse 9X 8.8000 9.2927 -0.4927 5.59946 Loral 139SX 7.6000 13.6984 -6.0984 80.24187 Loral 125 XT 6.8000 7.3809 -0.5809 8.54238 Motorola 190 3.2000 8.1290 -4.9290 154.03139 Sundstrand 500 1.7000 4.5546 -2.8546 167.919210 Sundstrand 40SL 1.6000 4.4715 -2.8715 179.4688





0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

0.0000 50.0000 100.0000 150.0000 200.0000

Std.

Res

idua

l

WT


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 50.0000 100.0000 150.0000 200.0000

Pred

icte

d (P

RICE

)

WT


Actual Predicted

49



Model Form: Unweighted Linear modelNumber of Observations Used: 10Equation in Unit Space: PRICE = 7.596 + 0.02471 * PWR




Intercept 7.5958 2.2298 3.4064 0.9907PWR 0.0247 0.0170 0.4567 1.4521 0.8155



6.2891 20.86% 10.97% 0.4567





Observations exhibiting unusual values Dependent Variable #1Independent Variable #2Observations influencing coefficients





Distance Flags1 22.2000 8.0900 14.1100 2.0980 2.3799 0.1113 0.3546 R2 17.3000 17.4809 -0.1809 6.1307 -0.1290 0.9503 0.1589 X3 11.8000 8.3371 3.4629 2.0501 0.5824 0.1063 0.02024 9.6000 7.8429 1.7571 2.1582 0.2975 0.1178 0.00595 8.8000 7.8429 0.9571 2.1582 0.1620 0.1178 0.00186 7.6000 10.0670 -2.4670 2.1062 -0.4163 0.1122 0.01097 6.8000 7.7440 -0.9440 2.1855 -0.1601 0.1208 0.00188 3.2000 7.7935 -4.5935 2.1717 -0.7783 0.1192 0.04109 1.7000 7.7935 -6.0935 2.1717 -1.0324 0.1192 0.072110 1.6000 7.6081 -6.0081 2.2260 -1.0214 0.1253 0.0747

SE = 6.2891, Mean = 9.0600, Coef. of Var. = 69.42% in Fit SpaceR denotes an observation with an unusual Dependent variable value.X denotes an observation with an unusual Independent variable value.

50

0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

0.0000 100.0000 200.0000 300.0000 400.0000 500.0000

Std.

Res

idua

l

PWR


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 100.0000 200.0000 300.0000 400.0000 500.0000

Pred

icte

d (P

RICE

)

PWR


Actual Predicted



Obs # Name Actuals Predicted Residuals % Errors Flags1 Motorola 200X 22.2000 8.0900 14.1100 -63.5585 @2 Sanders 212 17.3000 17.4809 -0.1809 1.0456 @3 Motorola GYX 11.8000 8.3371 3.4629 -29.34624 Sony 12B 9.6000 7.8429 1.7571 -18.30325 Westinghouse 9X 8.8000 7.8429 0.9571 -10.87626 Loral 139SX 7.6000 10.0670 -2.4670 32.46127 Loral 125 XT 6.8000 7.7440 -0.9440 13.88308 Motorola 190 3.2000 7.7935 -4.5935 143.54599 Sundstrand 500 1.7000 7.7935 -6.0935 358.439310 Sundstrand 40SL 1.6000 7.6081 -6.0081 375.5076





51



Model Form: Unweighted Linear modelNumber of Observations Used: 10Equation in Unit Space: PRICE = 6.562 + 0.009947 * FREQ




Intercept 6.5624 3.9803 1.6487 0.8623FREQ 0.0099 0.0133 0.2554 0.7473 0.5238



6.8350 6.52% -5.16% 0.2554





Observations exhibiting unusual values Dependent Variable Independent Variable Observations influencing coefficients #2





Distance Flags1 22.2000 10.5411 11.6589 2.9326 1.8884 0.1841 0.40232 17.3000 6.8608 10.4392 3.6514 1.8068 0.2854 0.6519 D3 11.8000 10.5411 1.2589 2.9326 0.2039 0.1841 0.00474 9.6000 10.5411 -0.9411 2.9326 -0.1524 0.1841 0.00265 8.8000 10.5411 -1.7411 2.9326 -0.2820 0.1841 0.00906 7.6000 6.8110 0.7890 3.7053 0.1374 0.2939 0.00397 6.8000 10.5411 -3.7411 2.9326 -0.6059 0.1841 0.04148 3.2000 8.1141 -4.9141 2.5048 -0.7727 0.1343 0.04639 1.7000 6.9801 -5.2801 3.5240 -0.9016 0.2658 0.147210 1.6000 9.1286 -7.5286 2.1634 -1.1612 0.1002 0.0751

SE = 6.8350, Mean = 9.0600, Coef. of Var. = 75.44% in Fit SpaceD denotes an observation with an unusual influence on the fitted regression equation.

52

0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000

Pred

icte

d (P

RICE

)

Actual


-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

0.0000 100.0000 200.0000 300.0000 400.0000 500.0000

Std.

Res

idua

l

FREQ


0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

0.0000 100.0000 200.0000 300.0000 400.0000 500.0000

Pred

icte

d (P

RICE

)

FREQ


Actual Predicted



Obs # Name Actuals Predicted Residuals % Errors Flags1 Motorola 200X 22.2000 10.5411 11.6589 -52.51772 Sanders 212 17.3000 6.8608 10.4392 -60.3424 @3 Motorola GYX 11.8000 10.5411 1.2589 -10.66884 Sony 12B 9.6000 10.5411 -0.9411 9.80295 Westinghouse 9X 8.8000 10.5411 -1.7411 19.78506 Loral 139SX 7.6000 6.8110 0.7890 -10.38127 Loral 125 XT 6.8000 10.5411 -3.7411 55.01588 Motorola 190 3.2000 8.1141 -4.9141 153.56449 Sundstrand 500 1.7000 6.9801 -5.2801 310.595610 Sundstrand 40SL 1.6000 9.1286 -7.5286 470.5396





53

Work Crew Productivity (regression)

The following data was collected on the productivity scores of depot maintenance work crews. Data was also obtained on crew size and bonus pay in an attempt to determine how these factors affected work crew productivity.

PRODUCTIVITY SCORE CREW SIZE BONUS PAY

42 4 2 39 4 2 48 4 3 51 4 3 49 6 2 53 6 2 61 6 3 60 6 3

54

Linear Analysis for Dataset WORK CREW PRODUCTIVITY


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: Score = 23.5 + 5.375 * Size




Prob Not Zero

Intercept 23.5000 10.1114 2.3241 0.0591 0.9409Size 5.3750 1.9830 0.7419 2.7105 0.0351 0.9649




5.6088 55.05% 47.55% 0.7419


Due To DFSum of Sqr

(SS)Mean SQ =




Obs # ScorePredicted Y



Distance Flags1 42.0000 45.0000 -3.0000 2.8044 -0.6176 0.2500 0.06362 39.0000 45.0000 -6.0000 2.8044 -1.2352 0.2500 0.25433 48.0000 45.0000 3.0000 2.8044 0.6176 0.2500 0.06364 51.0000 45.0000 6.0000 2.8044 1.2352 0.2500 0.25435 49.0000 55.7500 -6.7500 2.8044 -1.3896 0.2500 0.32196 53.0000 55.7500 -2.7500 2.8044 -0.5662 0.2500 0.05347 61.0000 55.7500 5.2500 2.8044 1.0808 0.2500 0.19478 60.0000 55.7500 4.2500 2.8044 0.8750 0.2500 0.1276

55



Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: Score = 27.25 + 9.25 * Pay




Prob Not Zero

Intercept 27.2500 11.6077 2.3476 0.0572 0.9428Pay 9.2500 4.5529 0.6384 2.0317 0.0884 0.9116




6.4388 40.76% 30.88% 0.6384


Due To DFSum of Sqr

(SS)Mean SQ =




Obs # ScorePredicted Y



Distance Flags1 42.0000 45.7500 -3.7500 3.2194 -0.6725 0.2500 0.07542 39.0000 45.7500 -6.7500 3.2194 -1.2105 0.2500 0.24423 48.0000 55.0000 -7.0000 3.2194 -1.2553 0.2500 0.26264 51.0000 55.0000 -4.0000 3.2194 -0.7173 0.2500 0.08585 49.0000 45.7500 3.2500 3.2194 0.5828 0.2500 0.05666 53.0000 45.7500 7.2500 3.2194 1.3002 0.2500 0.28177 61.0000 55.0000 6.0000 3.2194 1.0760 0.2500 0.19308 60.0000 55.0000 5.0000 3.2194 0.8967 0.2500 0.1340

56



Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: Score = 0.375 + 5.375 * Size + 9.25 * Pay




Prob Not Zero

Intercept 0.3750 4.7405 0.0791 0.9400 0.0600Size 5.3750 0.6638 0.7419 8.0974 0.0005 0.9995Pay 9.2500 1.3276 0.6384 6.9675 0.0009 0.9991




1.8775 95.80% 94.12% 0.9788


Due To DFSum of Sqr

(SS)Mean SQ =



Further Analysis of Variance

(SS explained by each variable when entered in the order given)

Due To DF Sequential SSRegression 2 402.2500Size 1 231.1250Pay 1 171.1250


Variables Score Size PayScore 1.0000 0.7419 0.6384Size 0.7419 1.0000 0.0000Pay 0.6384 0.0000 1.0000

57

Aircraft Engines (regression)

The engine program office has provided the following aircraft turbojet engine data. Each of the independent variables are expected to cause cost to increase, but the engineers are not certain as to whether to expect cost to increase in a linear manner, increase at an increasing rate, or increase at a decreasing rate.

Cost Thrust Weight Temp 10 7 18 100 25 8 44 105 30 17 57 90 30 13 67 85 50 22 112 110 60 34 112 105 75 39 128 95 80 39 165 100

58

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120 140 160 180

COST

WT

COST Vs. WTCorrelation Coefficient = 0.9779

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120

COST

TEMP

COST Vs. TEMPCorrelation Coefficient = 0.1697

Pairwise Variable Analysis For Dataset Engine Data

I. Correlation Matrix

COST TH WT TEMPCOST 1.0000 0.9760 0.9779 0.1697TH 0.9760 1.0000 0.9331 0.1202WT 0.9779 0.9331 1.0000 0.1963TEMP 0.1697 0.1202 0.1963 1.0000

II. Scatter Plot

0

10

20

30

40

50

60

70

80

90

0 5 10 15 20 25 30 35 40 45

COST

TH

COST Vs. THCorrelation Coefficient = 0.976

59

Linear Analysis for Dataset Engine Data


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = 3.76 + 1.843 * TH




Intercept 3.7599 4.2996 0.8745 0.5846TH 1.8431 0.1678 0.9760 10.9847 1.0000



5.9272 95.26% 94.47% 0.9760





Obs # COSTPredicted Y



Distance Flags1 10.0000 16.6618 -6.6618 3.3237 -1.3574 0.3144 0.42262 25.0000 18.5050 6.4950 3.1952 1.3010 0.2906 0.34673 30.0000 35.0932 -5.0932 2.2814 -0.9310 0.1482 0.07544 30.0000 27.7206 2.2794 2.6203 0.4287 0.1954 0.02235 50.0000 44.3088 5.6912 2.0965 1.0265 0.1251 0.07536 60.0000 66.4264 -6.4264 2.8629 -1.2382 0.2333 0.23337 75.0000 75.6421 -0.6421 3.4890 -0.1340 0.3465 0.00488 80.0000 75.6421 4.3579 3.4890 0.9095 0.3465 0.2193




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 16.6618 -6.6618 66.61832 25.0000 18.5050 6.4950 -25.98023 30.0000 35.0932 -5.0932 16.97724 30.0000 27.7206 2.2794 -7.59795 50.0000 44.3088 5.6912 -11.38236 60.0000 66.4264 -6.4264 10.71077 75.0000 75.6421 -0.6421 0.85618 80.0000 75.6421 4.3579 -5.4474




60


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = 0.9297 + 0.5015 * WT




Intercept 0.9297 4.3434 0.2141 0.1624WT 0.5015 0.0438 0.9779 11.4520 1.0000



5.6962 95.63% 94.90% 0.9779










Distance Flags1 10.0000 9.9569 0.0431 3.6633 0.0099 0.4136 0.00002 25.0000 22.9962 2.0038 2.7834 0.4032 0.2388 0.02553 30.0000 29.5159 0.4841 2.4257 0.0939 0.1813 0.00104 30.0000 34.5310 -4.5310 2.2117 -0.8632 0.1508 0.06615 50.0000 57.0990 -7.0990 2.2742 -1.3593 0.1594 0.17526 60.0000 57.0990 2.9010 2.2742 0.5555 0.1594 0.02937 75.0000 65.1231 9.8769 2.6727 1.9635 0.2202 0.5442 D8 80.0000 83.6790 -3.6790 3.9323 -0.8927 0.4766 0.3628




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 9.9569 0.0431 -0.43072 25.0000 22.9962 2.0038 -8.01523 30.0000 29.5159 0.4841 -1.61384 30.0000 34.5310 -4.5310 15.10325 50.0000 57.0990 -7.0990 14.19796 60.0000 57.0990 2.9010 -4.83517 75.0000 65.1231 9.8769 -13.1692 @8 80.0000 83.6790 -3.6790 4.5988





61


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = (-5.641) + 0.5128 * TEMP




Intercept -5.6410 120.4090 -0.0468 0.0358TEMP 0.5128 1.2155 0.1697 0.4219 0.3122



26.8384 2.88% -13.31% 0.1697








Distance Flags1 10.0000 45.6410 -35.6410 9.6097 -1.4223 0.1282 0.14872 25.0000 48.2051 -23.2051 12.1554 -0.9698 0.2051 0.12143 30.0000 40.5128 -10.5128 14.2535 -0.4623 0.2821 0.04204 30.0000 37.9487 -7.9487 19.2194 -0.4243 0.5128 0.09485 50.0000 50.7692 -0.7692 16.6445 -0.0365 0.3846 0.00046 60.0000 48.2051 11.7949 12.1554 0.4929 0.2051 0.03147 75.0000 43.0769 31.9231 10.5269 1.2931 0.1538 0.15208 80.0000 45.6410 34.3590 9.6097 1.3711 0.1282 0.1382




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 45.6410 -35.6410 356.41032 25.0000 48.2051 -23.2051 92.82053 30.0000 40.5128 -10.5128 35.04274 30.0000 37.9487 -7.9487 26.49575 50.0000 50.7692 -0.7692 1.53856 60.0000 48.2051 11.7949 -19.65817 75.0000 43.0769 31.9231 -42.56418 80.0000 45.6410 34.3590 -42.9487




62


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = 0.8306 + 0.9282 * TH + 0.2663 * WT




Intercept 0.8306 2.5428 0.3266 0.2428TH 0.9282 0.2625 0.4915 3.5366 0.9834WT 0.2663 0.0713 0.5192 3.7358 0.9865



3.3347 98.75% 98.25% 0.9937





Variables COST TH WTCOST 1.0000 0.9760 0.9779TH 0.9760 1.0000 0.9331WT 0.9779 0.9331 1.0000





Distance Flags1 10.0000 12.1214 -2.1214 2.2302 -0.8557 0.4473 0.19752 25.0000 19.9732 5.0268 1.8401 1.8075 0.3045 0.47683 30.0000 31.7890 -1.7890 1.5587 -0.6069 0.2185 0.03434 30.0000 30.7391 -0.7391 1.6811 -0.2566 0.2541 0.00755 50.0000 51.0762 -1.0762 2.1616 -0.4238 0.4202 0.04346 60.0000 62.2149 -2.2149 1.9660 -0.8223 0.3476 0.12017 75.0000 71.1167 3.8833 2.3066 1.6125 0.4784 0.79518 80.0000 80.9695 -0.9695 2.4262 -0.4238 0.5294 0.0673




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 12.1214 -2.1214 21.21432 25.0000 19.9732 5.0268 -20.10713 30.0000 31.7890 -1.7890 5.96354 30.0000 30.7391 -0.7391 2.46355 50.0000 51.0762 -1.0762 2.15246 60.0000 62.2149 -2.2149 3.69157 75.0000 71.1167 3.8833 -5.17788 80.0000 80.9695 -0.9695 1.2118




63


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = (-11.84) + 1.831 * TH + 0.1607 * TEMP




Intercept -11.8385 28.2667 -0.4188 0.3073TH 1.8311 0.1796 0.9696 10.1943 0.9998TEMP 0.1607 0.2874 0.0532 0.5592 0.3999



6.2990 95.54% 93.76% 0.9775





Variables COST TH TEMPCOST 1.0000 0.9760 0.1697TH 0.9760 1.0000 0.1202TEMP 0.1697 0.1202 1.0000





Distance Flags1 10.0000 17.0483 -7.0483 3.5991 -1.3634 0.3265 0.30042 25.0000 19.6828 5.3172 3.9959 1.0920 0.4024 0.26773 30.0000 33.7520 -3.7520 3.4104 -0.7085 0.2931 0.06944 30.0000 25.6243 4.3757 4.6700 1.0352 0.5497 0.43605 50.0000 46.1212 3.8788 3.9330 0.7883 0.3899 0.13246 60.0000 67.2904 -7.2904 3.4123 -1.3769 0.2935 0.26257 75.0000 74.8388 0.1612 3.9764 0.0330 0.3985 0.00028 80.0000 75.6423 4.3577 3.7078 0.8558 0.3465 0.1294




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 17.0483 -7.0483 70.48312 25.0000 19.6828 5.3172 -21.26873 30.0000 33.7520 -3.7520 12.50664 30.0000 25.6243 4.3757 -14.58585 50.0000 46.1212 3.8788 -7.75776 60.0000 67.2904 -7.2904 12.15077 75.0000 74.8388 0.1612 -0.21508 80.0000 75.6423 4.3577 -5.4472




64


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = 7.62 + 0.5038 * WT + (-0.06982) * TEMP




Intercept 7.6202 27.8595 0.2735 0.2046WT 0.5038 0.0486 0.9824 10.3593 0.9999TEMP -0.0698 0.2865 -0.0231 -0.2437 0.1828



6.2031 95.68% 93.95% 0.9781





Variables COST WT TEMPCOST 1.0000 0.9779 0.1697WT 0.9779 1.0000 0.1963TEMP 0.1697 0.1963 1.0000





Distance Flags1 10.0000 9.7071 0.2929 4.1189 0.0632 0.4409 0.00102 25.0000 22.4577 2.5423 3.7511 0.5146 0.3657 0.05093 30.0000 30.0550 -0.0550 3.4456 -0.0107 0.3085 0.00004 30.0000 35.4424 -5.4424 4.4487 -1.2590 0.5143 0.55955 50.0000 56.3696 -6.3696 3.8848 -1.3171 0.3922 0.37326 60.0000 56.7187 3.2813 2.9272 0.6000 0.2227 0.03447 75.0000 65.4783 9.5217 3.2551 1.8032 0.2754 0.41198 80.0000 83.7712 -3.7712 4.2990 -0.8433 0.4803 0.2191




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 9.7071 0.2929 -2.92922 25.0000 22.4577 2.5423 -10.16903 30.0000 30.0550 -0.0550 0.18324 30.0000 35.4424 -5.4424 18.14155 50.0000 56.3696 -6.3696 12.73926 60.0000 56.7187 3.2813 -5.46897 75.0000 65.4783 9.5217 -12.69568 80.0000 83.7712 -3.7712 4.7140




65


Model Form: Unweighted Linear modelNumber of Observations Used: 8Equation in Unit Space: COST = (-1.885) + 0.9368 * TH + 0.2632 * WT + 0.02833 * TEMP




Intercept -1.8853 16.9597 -0.1112 0.0832TH 0.9368 0.2973 0.4961 3.1516 0.9656WT 0.2632 0.0817 0.5131 3.2197 0.9677TEMP 0.0283 0.1744 0.0094 0.1624 0.1212



3.7160 98.76% 97.83% 0.9938





Variables COST TH WT TEMPCOST 1.0000 0.9760 0.9779 0.1697TH 0.9760 1.0000 0.9331 0.1202WT 0.9779 0.9331 1.0000 0.1963TEMP 0.1697 0.1202 0.1963 1.0000





Distance Flags1 10.0000 12.2429 -2.2429 2.5953 -0.8433 0.4878 0.16932 25.0000 20.1637 4.8363 2.3621 1.6859 0.4040 0.48173 30.0000 31.5914 -1.5914 2.1209 -0.5215 0.3257 0.03294 30.0000 30.3340 -0.3340 3.1193 -0.1654 0.7046 0.01635 50.0000 51.3162 -1.3162 2.8261 -0.5455 0.5784 0.10216 60.0000 62.4167 -2.4167 2.5187 -0.8845 0.4594 0.16627 75.0000 71.0282 3.9718 2.6275 1.5115 0.4999 0.57108 80.0000 80.9069 -0.9069 2.7310 -0.3599 0.5401 0.0380




Obs # Name Actuals Predicted Residuals % Errors Flags1 10.0000 12.2429 -2.2429 22.42912 25.0000 20.1637 4.8363 -19.34533 30.0000 31.5914 -1.5914 5.30464 30.0000 30.3340 -0.3340 1.11325 50.0000 51.3162 -1.3162 2.63256 60.0000 62.4167 -2.4167 4.02797 75.0000 71.0282 3.9718 -5.29578 80.0000 80.9069 -0.9069 1.1336




67

Wheel & Brake Assembly Spares (dummy variables) You are purchasing spare wheel and brake assemblies for an aircraft in the inventory. The assemblies are a new design, and incorporate advanced materials based on similar assemblies that the contractor sells commercially. The contractor has provided sales prices on their commercial brake assemblies, along with the associated rim diameters. They have based their proposed price for your brake assembly on a parametric model using rim diameter as the independent variable. Your engineer has confirmed rim diameter to be a logical explanatory variable and you have been able to validate the commercial sales prices. Determine if the proposed price of $19,319 for your 15” diameter brake assembly is fair and reasonable. What would you recommend for the government objective?

Price * Rim Diameter $9,000 10

$14,000 14 $18,000 16 $42,000 22 $45,000 24 $49,000 26 $54,000 28

* Based on an average price for a quantity of 15 units

68

Pairwise Variable Analysis For Dataset Wheels and Brakes I. Correlation Matrix Price RD LD Price 1.0000 0.9882 0.9704 RD 0.9882 1.0000 0.9262 LD 0.9704 0.9262 1.0000 II. Scatter Plot

Price Vs. RD Correlation Coefficient = 0.9882

0

10000

20000

30000

40000

50000

60000

0 5 10 15 20 25 30

Pric

e

RD

Price Vs. RD Correlation Coefficient = 0.9882

69

Linear Analysis for Dataset Wheels and Brakes


Model Form: Unweighted Linear modelNumber of Observations Used: 7Equation in Unit Space: Price = (-2.171e+004) + 2735 * RD




Intercept -21705.8824 3974.0135 -5.4620 0.9972RD 2735.2941 189.7002 0.9882 14.4190 1.0000



3128.6156 97.65% 97.18% 0.9882







Obs # PricePredicted Y



Distance Flags1 9000.0000 5647.0588 3352.9412 2235.3826 1.5318 0.5105 1.2235 D2 14000.0000 16588.2353 -2588.2353 1641.2864 -0.9717 0.2752 0.17933 18000.0000 22058.8235 -4058.8235 1405.0259 -1.4520 0.2017 0.26634 42000.0000 38470.5882 3529.4118 1241.8792 1.2291 0.1576 0.14135 45000.0000 43941.1765 1058.8235 1405.0259 0.3788 0.2017 0.01816 49000.0000 49411.7647 -411.7647 1641.2864 -0.1546 0.2752 0.00457 54000.0000 54882.3529 -882.3529 1923.9110 -0.3576 0.3782 0.0389


70

0.0000

10000.0000

20000.0000

30000.0000

40000.0000

50000.0000

60000.0000

0.000010000.000020000.000030000.000040000.000050000.000060000.0000

Pred

icte

d (P

rice)

Actual


-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000

Std.

Res

idua

l

RD


0.0000

10000.0000

20000.0000

30000.0000

40000.0000

50000.0000

60000.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000

Pred

icte

d (P

rice)

RD


Actual Predicted



Obs # Name Actuals Predicted Residuals % Errors Flags1 9000.0000 5647.0588 3352.9412 -37.2549 @2 14000.0000 16588.2353 -2588.2353 18.48743 18000.0000 22058.8235 -4058.8235 22.54904 42000.0000 38470.5882 3529.4118 -8.40345 45000.0000 43941.1765 1058.8235 -2.35296 49000.0000 49411.7647 -411.7647 0.84037 54000.0000 54882.3529 -882.3529 1.6340

Avg (Arith) 33000.0000 33000.0000 0.0000 -0.64%Avg (Absolute) 2268.9076 13.07%




71

Log Linear Analysis for Dataset Wheels and Brakes


Model Form: Unweighted Log-Linear modelNumber of Observations Used: 7Equation in Unit Space: Price = 401.7 * RD ^ 1.356 * 1.504 ^ LD




Intercept 5.9957 0.2672 22.4422 1.0000RD 1.3563 0.1035 0.7148 13.1033 0.9998EXP_LD 0.4080 0.0733 0.3038 5.5678 0.9949



0.0400 99.79% 99.69% 0.9990





Observations exhibiting unusual values Dependent Variable Independent Variable Observations influencing coefficients #1, #3


Obs # Log of PricePredicted Y



Distance Flags1 9.1050 9.1187 -0.0137 0.0362 -0.7995 0.8163 0.9466 D2 9.5468 9.5751 -0.0282 0.0242 -0.8844 0.3639 0.14923 9.7981 9.7562 0.0420 0.0311 1.6650 0.6038 1.4083 D4 10.6454 10.5961 0.0493 0.0238 1.5302 0.3524 0.42485 10.7144 10.7141 0.0003 0.0204 0.0082 0.2590 0.00006 10.7996 10.8227 -0.0231 0.0205 -0.6724 0.2625 0.05367 10.8967 10.9232 -0.0265 0.0234 -0.8149 0.3420 0.1151


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Obs # Name Actuals Predicted Residuals % Errors Flags1 9000.0000 9124.3462 -124.3462 1.3816 @2 14000.0000 14401.0350 -401.0350 2.86453 18000.0000 17260.2701 739.7299 -4.1096 @4 42000.0000 39979.4717 2020.5283 -4.81085 45000.0000 44987.2336 12.7664 -0.02846 49000.0000 50146.0553 -1146.0553 2.33897 54000.0000 55448.3376 -1448.3376 2.6821

Avg (Arith) 33000.0000 33049.5356 -49.5356 0.05%Avg (Absolute) 841.8284 2.60%




0.0000

10000.0000

20000.0000

30000.0000

40000.0000

50000.0000

60000.0000

0.000010000.000020000.000030000.000040000.000050000.000060000.0000

Pred

icte

d (P

rice)

Actual


-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

0.0000 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000

Std.

Res

idua

l

RD


0.0000

10000.0000

20000.0000

30000.0000

40000.0000

50000.0000

60000.0000

0.0000 5.0000 10.0000 15.0000 20.0000 25.0000 30.0000

Pred

icte

d (P

rice)

RD


Actual Predicted

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