Template:Example: Lognormal General Example Complete Data: Difference between revisions

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(Created page with ''''Lognormal Distribution General Example Complete Data''' Determine the lognormal parameter estimates for the data given in Table 9.4. {|align="center" border=1 cellspacing=1 …')
 
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{|align="center" border=1 cellspacing=1  
{|align="center" border=1 cellspacing=1  
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|colspan="3" style="text-align:center"| Table 9.4 - Non-Grouped Data for Example 12
|colspan="3" style="text-align:center"| Table 9.4 - Non-Grouped Times-to-Failure Data
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!Data point index
!Data point index
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::<math>\begin{align}
::<math>\begin{align}
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
  & {{{\hat{\sigma }}}_{{{T}'}}}= & 1.10   
  & {{{\hat{\sigma '}}}_}= & 1.10   
\end{align}</math>
\end{align}</math>


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::<math>\begin{align}
::<math>\begin{align}
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
  & {{{\hat{\sigma }}}_{{{T}'}}}= & 1.24   
  & {{{\hat{\sigma' }}}}= & 1.24   
\end{align}</math>
\end{align}</math>


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::<math>\begin{align}
::<math>\begin{align}
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
   & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\  
  & {{{\hat{\sigma }}}_{{{T}'}}}= & 1.36   
  & {{{\hat{\sigma' }}}}= & 1.36   
\end{align}</math>
\end{align}</math>

Revision as of 23:44, 13 February 2012

Lognormal Distribution General Example Complete Data

Determine the lognormal parameter estimates for the data given in Table 9.4.

Table 9.4 - Non-Grouped Times-to-Failure Data
Data point index State F or S State End Time
1 F 2
2 F 5
3 F 11
4 F 23
5 F 29
6 F 37
7 F 43
8 F 59

Solution

Using Weibull++, the computed parameters for maximum likelihood are:

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\ & {{{\hat{\sigma '}}}_}= & 1.10 \end{align} }[/math]


For rank regression on [math]\displaystyle{ X\ \ : }[/math]

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\ & {{{\hat{\sigma' }}}}= & 1.24 \end{align} }[/math]


For rank regression on [math]\displaystyle{ Y\ \ : }[/math]

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 2.83 \\ & {{{\hat{\sigma' }}}}= & 1.36 \end{align} }[/math]