In a component manufacturing industry, there is a small probability for any
component to be defective. The components are supplied in packets of . Use
Poisson approximation to calculate appropriately the number of packets containing
(i) at least one defective (ii) at most one defective in a consignment of 100 packets
The number of defective blades in a packet has binomial distribution 𝐵(𝑛, 𝑝) with
parameters 𝑛 = 100 and 𝑝 = 0.0002
The binomial distribution can be approximated using Poisson with parameter 𝑚 =
𝑛𝑝 = 0.02
Let 𝑋 equals the number of defective blades in a packet.
"\ud835\udc5d_0 = \ud835\udc43(\ud835\udc4b = 0) = \ud835\udc52^{-0.02} = 0.9802"
Using the formula "\ud835\udc5d_{\ud835\udc65+1 }= \ud835\udc5d\ud835\udc65 \u22c5\n\n\\frac{\ud835\udc5a\n}{\n\ud835\udc65 + 1}"
we have:
"\ud835\udc5d_1 = \ud835\udc5d_0 \u22c5 \\frac{\n0.02}{\n\n1} = 0.019604"
"\ud835\udc5d_2 = \ud835\udc5d_1 \u22c5\\frac{\n\n0.02}{\n\n2} = 0.009802"
"\ud835\udc5d_3 = \ud835\udc5d_2 \u22c5 \\frac{\n\n0.02}{\n\n3}\u2248 0"
Thus expected frequencies are:
(i) at least one defective
"\ud835\udc5b_1+\ud835\udc5b_2+\ud835\udc5b_3 =100 \u22c5 \ud835\udc5d_1+100 \u22c5 \ud835\udc5d_2+100 \u22c5 \ud835\udc5d_3 \u2248 1.96+0.9802+0 \u2248 2.9402"
ii) at most one defective
"\ud835\udc5b_0+\ud835\udc5b_1=100 \u22c5 \ud835\udc5d_0+100 \u22c5 \ud835\udc5d_1 \u2248 0.019604+0.009802 \u2248 0.029406"
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