Difference between revisions of "Non-Parametric RDA Transmission Example"

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The data is entered into a Non-Parametric RDA Specialized Folio in Weibull++ as follows.  
 
The data is entered into a Non-Parametric RDA Specialized Folio in Weibull++ as follows.  
  
[[Image:systemRDA1.png|thumb|center|400px| ]]
+
[[Image:Recurrent Data Example 3 Data.png|thumb|center|400px| ]]
  
 
The results are as follows,
 
The results are as follows,
  
[[Image:spreadsheetplain.png|thumb|center|400px| ]]  
+
[[Image:Recurrent Data Example 3 Result.png|thumb|center|400px| ]]  
  
 
The results indicate that after 13,957 miles of testing, the estimated mean cumulative number of repairs per car is 0.5. Therefore, by 24,000 test miles, the estimated mean cumulative number of repairs per car is 0.5.
 
The results indicate that after 13,957 miles of testing, the estimated mean cumulative number of repairs per car is 0.5. Therefore, by 24,000 test miles, the estimated mean cumulative number of repairs per car is 0.5.
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The MCF plot is shown next.
 
The MCF plot is shown next.
  
[[Image:lda11.8.gif|thumb|center|400px| ]]  
+
[[Image:Recurrent Data Example 3 Plot.png|thumb|center|400px| ]]  
  
 
A smooth curve through the MCF plot has a derivative that decreases as the population ages. That is, the repair rate decreases as each population ages. This is typical of products with manufacturing defects.
 
A smooth curve through the MCF plot has a derivative that decreases as the population ages. That is, the repair rate decreases as each population ages. This is typical of products with manufacturing defects.

Revision as of 21:41, 2 March 2012

Recurrent Events Data Non-parameteric Transmission Example

The following table displays transmission repairs on a sample of 14 cars with manual transmission in a preproduction road test [31]. Here + denotes the censoring ages (how long a car has been observed).

[math]\begin{matrix} Car ID & Mileage \\ \text{1} & \text{27099+} \\ \text{2} & \text{21999+} \\ \text{3} & \text{11891, 27583+} \\ \text{4} & \text{19966+} \\ \text{5} & \text{26146+} \\ \text{6} & \text{3648, 13957, 23193+} \\ \text{7} & \text{19823+} \\ \text{8} & \text{2890, 22707+} \\ \text{9} & \text{2714, 19275+} \\ \text{10} & \text{19803+} \\ \text{11} & \text{19630+} \\ \text{12} & \text{22056+} \\ \text{13} & \text{22940+} \\ \text{14} & \text{3240, 7690, 18965+} \\ \end{matrix}[/math]

The car manufacturer seeks to estimate the mean cumulative number of repairs per car by 24,000 test miles (equivalently 5.5 x 24,000 = 132,000 customer miles) and to observe whether the population repair rate increases or decreases as a population ages.


Solution

The data is entered into a Non-Parametric RDA Specialized Folio in Weibull++ as follows.

Recurrent Data Example 3 Data.png

The results are as follows,

Recurrent Data Example 3 Result.png

The results indicate that after 13,957 miles of testing, the estimated mean cumulative number of repairs per car is 0.5. Therefore, by 24,000 test miles, the estimated mean cumulative number of repairs per car is 0.5.

The MCF plot is shown next.

Recurrent Data Example 3 Plot.png

A smooth curve through the MCF plot has a derivative that decreases as the population ages. That is, the repair rate decreases as each population ages. This is typical of products with manufacturing defects.