Hello,

I am attempting to develop a procedure to reduce the total number of trials while still being assured that the a distribution's parameters are accurate.

For example... We have a machine that is producing cogs with mean diameter 12 and standard deviation 1. Ho is that mean and variation = 12, 1 and H1 is that mean and variation do not equal 12,1.

I am pretty sure the answer to this question is about 5 trials will give you an a = .05.

I would like to translate this into a form where both mean and variation are unknown (or any distributions parameters) and as you take measurements you can make a statement to how well you believe to know these parameters.

To do this I think I can use LRT because...

1) Lack of dependence on large data sets (unlike Chi-Squared testing...)

2) Ability to compare likelihood of different distributions

I would appreciate some help or links to good examples (I have searched a lot) on how to find the C value of the test so that I can set a = .05 and determine the number of trials needed.

I guess basically to say that 95% of the time i take n number of measurements I will have an accuracy of x within the actual value of the estimated parameter.

Also any advice as that I am headed in the right direction would be good

Also why is c bounded between 0 and 1 when LRT is bounded between 0 and infinity?

Thanks

I am attempting to develop a procedure to reduce the total number of trials while still being assured that the a distribution's parameters are accurate.

For example... We have a machine that is producing cogs with mean diameter 12 and standard deviation 1. Ho is that mean and variation = 12, 1 and H1 is that mean and variation do not equal 12,1.

I am pretty sure the answer to this question is about 5 trials will give you an a = .05.

I would like to translate this into a form where both mean and variation are unknown (or any distributions parameters) and as you take measurements you can make a statement to how well you believe to know these parameters.

To do this I think I can use LRT because...

1) Lack of dependence on large data sets (unlike Chi-Squared testing...)

2) Ability to compare likelihood of different distributions

I would appreciate some help or links to good examples (I have searched a lot) on how to find the C value of the test so that I can set a = .05 and determine the number of trials needed.

I guess basically to say that 95% of the time i take n number of measurements I will have an accuracy of x within the actual value of the estimated parameter.

Also any advice as that I am headed in the right direction would be good

Also why is c bounded between 0 and 1 when LRT is bounded between 0 and infinity?

Thanks

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