<p> plot(data,char,size,clr)</p>

f7d86ce1-1d50-4744-89d5-cf92f53cd3a3 19 4 5 decimal

&[DATE] &[TIME] - &[FILENAME]

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 31 line for data r3 r4 r5 augment 21 line :

Plant Quality Control

Since the sampling is done without replacement, the probability of finding a given number of defectives is not well modeled by the Poisson distribution when the sample size is an appreciable fraction of the lot size. In this case, the Hypergeometric distribution provides a good model for the sampling process.

Lot size N 40 :

number of defectives D 0 N range :

sample size n 8 :

acceptance criterium c 2 :

The statistical Hypergeometric

n k cb n! k! (n k - (! * / n k ≥ 0 cases :

N n D u H D u cb N D - n u - cb * N n cb / :

D PA 1 D c ≤ c i - (N D - (≤ (N n D i H * (i 0 c sum cases :

Lot Acceptance Probability LAP D N / D PA vectorize augment :

Average Outgoing Quality AOQ D N / D PA D N / * vectorize augment :

Total Average Inspection ati D PA n * vectorize 1 D PA - (N * vectorize + : ATI D N / ati augment : 21 sys

Lot Acceptance Probability vs defect rate

LAP LAP o 5 red plot 2 1 sys

Average Outgoing Quality vs defect rate

AOQ AOQ o 5 red plot 2 1 sys

Average Total Inspection vs defect rate

ATI n ATI o 5 red plot 3 1 sys