Hi,
I have 10 data points which are measurements (say height for example). I did a goodness of fit test and found Weibull to be the best fit. I also got a shape and scale parameter for this fit. Using these parameters, I generated random numbers in excel that fit this Weibull distribution. I copied this down to about 10000 cells to get myself something of a monte carlo type simulation.
My question is, is it fair to keep using this shape and scale parameter? What if another data set doesn't match the shape and scale parameter I am using in my simulation, would that not make my results (from the simulation) invalid?
Now what I would rather have is to specify in my spreadsheet, a mean and sigma and for the excel to generate random numbers that fit a weibull distribution and then use this to calculate the shape and scale parameter. How would I do this? I would really appreciate it if the theory is supported with an example as it is easier for me to understand (rather than looking at equations).
Thanks in advance !
I have 10 data points which are measurements (say height for example). I did a goodness of fit test and found Weibull to be the best fit. I also got a shape and scale parameter for this fit. Using these parameters, I generated random numbers in excel that fit this Weibull distribution. I copied this down to about 10000 cells to get myself something of a monte carlo type simulation.
My question is, is it fair to keep using this shape and scale parameter? What if another data set doesn't match the shape and scale parameter I am using in my simulation, would that not make my results (from the simulation) invalid?
Now what I would rather have is to specify in my spreadsheet, a mean and sigma and for the excel to generate random numbers that fit a weibull distribution and then use this to calculate the shape and scale parameter. How would I do this? I would really appreciate it if the theory is supported with an example as it is easier for me to understand (rather than looking at equations).
Thanks in advance !