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 Table | Determine the lognormal parameter estimates for the data given in the following Table. | ||
{|align="center" border=1 cellspacing=1 | {|align="center" border=1 cellspacing=1 | ||
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|colspan="3" style="text-align:center"| Table | |colspan="3" style="text-align:center"| Table - Non-Grouped Times-to-Failure Data | ||
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!Data point index | !Data point index |
Revision as of 17:04, 2 March 2012
Lognormal Distribution General Example Complete Data
Determine the lognormal parameter estimates for the data given in the following Table.
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]