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

f7d86ce1-1d50-4744-89d5-cf92f53cd3a3 21 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

Global D N / LAP 2 col AOQ 2 col ATI 2 col augment :

Lot Acceptance Probability vs defect rate

LAP LAP o 5 red plot 2 1 sys

Global 0108 0.025 1 0.025 8 0.05 1 0.05 8 0.075 0.9943 0.0746 8.1814 0.1 0.9796 0.098 8.652 0.125 0.9543 0.1193 9.4625 0.15 0.9181 0.1377 10.6203 0.175 0.8718 0.1526 12.1016 0.2 0.8168 0.1634 13.8612 0.225 0.755 0.1699 15.8408 0.25 0.6883 0.1721 17.9755 0.275 0.6188 0.1702 20.1991 0.3 0.5485 0.1645 22.4483 0.325 0.4792 0.1557 24.6654 0.35 0.4125 0.1444 26.8003 0.375 0.3496 0.1311 28.8121 0.4 0.2916 0.1166 30.6691 0.425 0.2391 0.1016 32.3493 0.45 0.1925 0.0866 33.8395 0.475 0.152 0.0722 35.135 0.5 0.1176 0.0588 36.2381 0.525 0.0888 0.0466 37.1574 0.55 0.0654 0.036 37.9061 234 mat

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