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"Backsolve" Poisson function
I am trying to estimate the number of spare parts required to give say
95% protection level. At the moment I have used the Poisson function to calculate the protection level with various number of spares. I then use MATCH to find the first value that exceeds the required protection level. Is there a way of reversing the POISSON calculation so that I input protection level plus Mean demand (cumulative = True) and output the number of spare parts required? |
"Backsolve" Poisson function
View this site:
http://support.microsoft.com/kb/828130 Copy/paste data into a .txt file and then copy/paste from there into a worksheet. In Cell D3 enter: =C3*$B$1 Fill down... Would this work for you? Regards, Ryan--- -- RyGuy "SteveG" wrote: I am trying to estimate the number of spare parts required to give say 95% protection level. At the moment I have used the Poisson function to calculate the protection level with various number of spares. I then use MATCH to find the first value that exceeds the required protection level. Is there a way of reversing the POISSON calculation so that I input protection level plus Mean demand (cumulative = True) and output the number of spare parts required? |
"Backsolve" Poisson function
Both my original technique and this new technique use an intermediate
table. I was trying to avoid a table of pre-calculated results. Thanks |
"Backsolve" Poisson function
Mathematically, =POISSON(x,m,TRUE) is equivalent to each of the following
expressions =CHIDIST(2*m,2*(x+1)) =1-GAMMADIST(m,x+1,1,TRUE) So you can use CHIINV and GAMMAINV to obtain Poisson confidence limits. Numerically, this will fail if m needs to be too large, because of weaknesses in Excel's implementation of CHIDIST, GAMMADIST, and their inverses. A world-class (for both accuracy and working range) library of VBA probability functions is available in http://members.aol.com/iandjmsmith/examples.xls which also explicitly includes confidence limit functions for discrete distributions. Jerry "SteveG" wrote: I am trying to estimate the number of spare parts required to give say 95% protection level. At the moment I have used the Poisson function to calculate the protection level with various number of spares. I then use MATCH to find the first value that exceeds the required protection level. Is there a way of reversing the POISSON calculation so that I input protection level plus Mean demand (cumulative = True) and output the number of spare parts required? |
"Backsolve" Poisson function
This is where my Knowledge Stats < Knowledge Excel! (I inherited a
spreadsheet using Poisson.) In the poisson calculation I have Probability = POISSON(x,m,TRUE) where x is the number of spares and m is the 'demand'. Ideally I would reverse this calculation inputing the required probability and obtain x (the spares level required). What you show is Probability =CHIDIST(X,degrees_freedom)= CHIDIST(2*m,2*(x+1) or Probability =1- GAMMADIST(X,alpha,beta,cumulative)=1-GAMMADIST(m,x+1,1,TRUE). When I turn to CHIINV(probability,degrees_freedom) or GAMMAINV(probability,alpha,beta) I still see the degrees of freedom or Alpha parameters as inputs when they contain the x number of spares. This leaves me confused ... .... But fortunately in the examples.xls sheet I see the crit_poisson(B70,D70) calculation which fortunately seems to match my original calculations! So I will probably use this! Thanks Steve |
Quote:
This leaves me confused too! I guess I'm faced with a simular problem as yours. I am trying to estimate the number of spare parts required to keep in stock to give me a 90% Service level (protection level). At the moment, the reverse calculation for demand with normal distribution is no problem ( x = NORMSINV ("ServiceLevel") ) however I could not find such for Poisson demand. So, simular to your earlier question; input = Service level and Mean demand (cumulative = True) and output the number of spare parts required? I follow your reasoning and I fully agree with your conclusion; Quote:
Can anyone help me to solve this using Excel 2010 and/or by not using the "crit_poisson()" function? Thanks a lot! KRG Erik |
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