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

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'''Lognormal Distribution General Example Complete Data'''
'''Lognormal Distribution General Example Complete Data'''


Determine the lognormal parameter estimates for the data given in the following Table.
Determine the lognormal parameter estimates for the data given in the following table.
{|align="center" border=1 cellspacing=1
{|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
|-
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|colspan="3" style="text-align:center"| Table - Non-Grouped Times-to-Failure Data
|colspan="3" style="text-align:center"| Non-Grouped Times-to-Failure Data
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!Data point index
!Data point index
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  & {\hat{\sigma '}}= & 1.10   
  & {\hat{\sigma '}}= & 1.10   
\end{align}</math>
\end{align}</math>


For rank regression on  <math>X</math>   
For rank regression on  <math>X</math>   
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  & {{{\hat{\sigma' }}}}= & 1.24   
  & {{{\hat{\sigma' }}}}= & 1.24   
\end{align}</math>
\end{align}</math>


For rank regression on  <math>Y:</math>   
For rank regression on  <math>Y:</math>   

Revision as of 04:52, 8 August 2012

Lognormal Distribution General Example Complete Data

Determine the lognormal parameter estimates for the data given in the following table.

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]