Example of Parametric Distribution Analysis (Arbitrary Censoring)

A reliability engineer wants to assess the reliability of a new type of muffler and to estimate the proportion of warranty claims that can be expected with a 50,000-mile warranty. The engineer collects failure data on both the old type and the new type of mufflers. Mufflers were inspected for failure every 10,000 miles.

The engineer records the number of failures for each 10,000-mile interval. Therefore, the data are arbitrarily censored. The engineer uses Parametric Distribution Analysis (Arbitrary Censoring) to determine the following:
  • The mileage at which various percentages of the mufflers fail
  • The percentage of mufflers that will survive past 50,000 miles
  • The survival function for the mufflers (as shown on a survival plot)
  • The fit of the Weibull distribution for the data (as shown on a probability plot)
  1. Open the sample data, MufflerReliability.MTW.
  2. Choose Stat > Reliability/Survival > Distribution Analysis (Arbitrary Censoring) > Parametric Distribution Analysis.
  3. In Start variables, enter StartOld StartNew.
  4. In End variables, enter EndOld EndNew.
  5. In Frequency columns (optional), enter FreqOld FreqNew.
  6. From Assumed distribution, select Weibull.
  7. Click Estimate. In Estimate probabilities for these times (values), enter 50000. Click OK.
  8. Click Graphs. Select Survival plot.
  9. Click OK in each dialog box.

Interpret the results

Using the Table of Percentiles, the engineer can determine the mileage at which various percentages of the old mufflers and new mufflers fail. For the old mufflers, 10% of the mufflers fail by 38,307 miles. For the new mufflers, 10% of the mufflers to fail by 56,006.1 miles.

Using the Table of Survival Probabilities, the engineer can determine what proportion of the mufflers are expected to survive at least 50,000 miles. For the old mufflers, the probability of surviving past 50,000 miles is approximately 75.07%. For the new mufflers, the probability of surviving past 50,000 miles is approximately 94.67%.

The engineer uses the survival plot to view the survival probabilities at different mileages, and the probability plot to check that the Weibull distribution adequately fits the data.

Old Mufflers
Variable Start: StartOld  End: EndOld
Frequency: FreqOld

Censoring

Censoring InformationCount
Right censored value83
Interval censored value965
Left censored value1
Estimation Method: Maximum Likelihood
Distribution:   Weibull

Parameter Estimates



Standard
Error
95.0% Normal CI
ParameterEstimateLowerUpper
Shape3.758790.1002263.567393.96045
Scale69708.9618.00068508.170930.7
Log-Likelihood = -2083.927

Goodness-of-Fit

Anderson-Darling
(Adjusted)
1.703

Characteristics of Distribution



Standard
Error
95.0% Normal CI

EstimateLowerUpper
Mean(MTTF)62963.8585.83461826.064122.5
Standard Deviation18685.0417.81217883.819522.1
Median63232.6618.04862032.764455.6
First Quartile(Q1)50042.1692.16248703.751417.3
Third Quartile(Q3)76037.5658.03774758.677338.2
Interquartile Range(IQR)25995.4610.47824826.027219.9

Table of Percentiles



Standard
Error
95.0% Normal CI
PercentPercentileLowerUpper
120501.3730.97319117.521985.2
224686.2762.13823236.726226.0
327535.4773.44126060.529093.8
429766.4777.50728280.831329.9
531630.7778.04030141.933193.0
633249.1776.58931761.334806.5
734689.8773.92633205.636240.3
835995.3770.48834516.437537.6
937194.3766.53735721.938727.5
1038307.0762.24336841.839830.5
2046771.7714.66245391.848193.6
3052987.5671.73551687.154320.5
4058301.0638.54457062.859566.1
5063232.6618.04862032.764455.6
6068106.3614.50066912.569321.4
7073237.9634.99772003.874493.1
8079117.5693.24477770.380487.9
9087026.8827.62085419.888664.1
9188068.9849.54786419.589749.8
9289195.0874.22687497.990925.0
9390425.9902.32388674.692211.8
9491791.7934.80889977.793642.3
9593338.0973.16291450.095265.0
9695139.21019.8393161.297159.2
9797330.71079.3195238.299469.3
981002061161.4797954.9102508
991046501296.79102139107223

Table of Survival Probabilities



95.0% Normal CI
TimeProbabilityLowerUpper
500000.7506820.7279110.771856
New Mufflers
Variable Start: StartNew  End: EndNew
Frequency: FreqNew
* NOTE * 8 cases were used
* NOTE * 2 cases contained missing values or was a case with zero frequency.

Censoring

Censoring InformationCount
Right censored value210
Interval censored value839
Estimation Method: Maximum Likelihood
Distribution:   Weibull

Parameter Estimates



Standard
Error
95.0% Normal CI
ParameterEstimateLowerUpper
Shape5.767700.1743615.435896.11977
Scale82733.7501.28581757.083722.0
Log-Likelihood = -1804.510

Goodness-of-Fit

Anderson-Darling
(Adjusted)
7.278

Characteristics of Distribution



Standard
Error
95.0% Normal CI

EstimateLowerUpper
Mean(MTTF)76585.0488.71075633.177548.8
Standard Deviation15389.5407.42114611.416209.1
Median77639.9501.31276663.578628.7
First Quartile(Q1)66660.6610.00165475.767866.9
Third Quartile(Q3)87554.2543.21586496.088625.4
Interquartile Range(IQR)20893.7591.84419765.322086.5

Table of Percentiles



Standard
Error
95.0% Normal CI
PercentPercentileLowerUpper
137265.1938.48535470.339150.6
242060.6910.59040313.243883.7
345163.8884.87143462.446931.9
447516.0861.88645856.449235.7
549434.9841.14747813.551111.3
651068.9822.21949482.652706.1
752500.3804.77650946.554101.6
853779.7788.57252256.155347.7
954940.5773.42453445.356477.5
1056006.1759.18654537.757514.0
2063788.2649.87362527.165074.7
3069192.0576.97968070.370332.1
4073638.2528.30272609.974680.9
5077639.9501.31276663.578628.7
6081489.1497.21280520.482469.5
7085439.7519.74784427.086464.5
8089849.4577.13288725.490987.7
9095605.5695.27994252.596978.0
9196350.1713.48094961.897758.6
9297151.1733.70495723.798599.9
9398022.8756.42996551.499516.6
9498985.2782.34097463.6100530
95100069812.48898488.8101674
96101323848.59599673.3103000
97102838893.813101101104605
98104808955.006102952106696
991078141053.11105770109898

Table of Survival Probabilities



95.0% Normal CI
TimeProbabilityLowerUpper
500000.9467040.9359960.955664