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What happens if you select the wrong distribution?
If you select the wrong distribution, your calculations against the specifications will not accurately reflect what the process produces. Various distributions are usually tested against the data to determine which one best fits the data. You can’t just look at the shape of the distribution and assume it is a good fit to your data.
How does the normal distribution of random numbers work out?
The numbers work out as follows: Given a population, 68% of the members of that population will have values in the range of one standard deviation from the mean, 95% within two standard deviations, and 99.7% within three standard deviations.
How to find a random number from a name?
R = random(‘name’,A) returns a random number from the one-parameter distribution family specified by ‘name’ and the distribution parameter A. example. R = random(‘name’,A,B) returns a random number from the two-parameter distribution family specified by ‘name’ and the distribution parameters A and B.
Are there any distributions that have the same parameters?
Not all distributions have the same parameters. For example, the normal distribution is described by the location and the scale while the Gamma distribution is described by the shape and scale. The parameters in Table 1 minimized the negative log-likelihood for each distribution.
What can you do with normal distribution data?
Normal distribution is a means to an end, not the end itself. Normally distributed data is needed to use a number of statistical tools, such as individuals control charts, Cp / Cpk analysis, t -tests and the analysis of variance (ANOVA).
Why is it important to choose the right distribution?
It is important to have the distribution that accurately reflects your data. If you select the wrong distribution, your calculations against the specifications will not accurately reflect what the process produces. Various distributions are usually tested against the data to determine which one best fits the data.
What to do when your data is not normal?
For example, to bound anything with 95% confidence, you need to include data up to 4.5 standard deviations vs. only 2 standard deviations (for Normal). But it can still save the day when the data looks nothing like a Normal distribution. Is there anything better?