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Terms in this set (6)
Required Sample Sizes (n) are determined by
-The desired type I and type II risks
-the minimum value to be detected between the population mean parameters
- the variation in the characteristic being measured
poisson
discrete frequency distribution
- Can be used as an approximation to the binomial
- For np<5
n- sample size p= probability
- can be different types of defects
- cab be infinite amount of N
binomial
discrete frequency distribution
- Sampling with replacement
- The sample size is less than 10% of N
-requires larege population
The normal distribution the binomial when np>=5
-fixed number of N
- can only be pass/fail go/nogo
Hypergeometric
discrete frequency distribution
- Sampling is without replacement
- the population is frequently small
- Applied when sample is relatively large proportion of the population
- attempt 1 is dependent of attempt 2
confidence level
large samples
-use the normal distribution
small samples
- t distribution
central limit theory
The central limit theorem (CLT) is a statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore, all of the samples will follow an approximate normal distribution pattern, with all variances being approximately equal to the variance of the population divided by each sample's size.
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