What is the Probability of 3 Defective Plastic Containers in a Sample of 10,000?

  • Thread starter Thread starter rsala004
  • Start date Start date
Click For Summary
SUMMARY

The discussion focuses on calculating the probability of finding exactly 3 defective plastic containers in a sample of 10,000, where the defect rate is 0.03%. The correct approach involves using the Poisson distribution, with λ set to 3, derived from the defect rate multiplied by the sample size. The final probability, P(X=3), is confirmed to be 0.224. The initial confusion stemmed from misinterpreting the defect rate as 3% instead of 0.03%.

PREREQUISITES
  • Understanding of Poisson distribution and its applications
  • Familiarity with binomial coefficients and calculations
  • Basic knowledge of probability theory
  • Ability to perform factorial calculations
NEXT STEPS
  • Study the derivation and application of the Poisson distribution in real-world scenarios
  • Learn how to calculate binomial probabilities using binomial coefficients
  • Explore the differences between binomial and Poisson distributions
  • Practice solving probability problems involving large sample sizes and small probabilities
USEFUL FOR

Students studying probability theory, statisticians, and professionals involved in quality control and manufacturing processes.

rsala004
Messages
23
Reaction score
0

Homework Statement



Suppose that 0.03% of plastic containers manufactured by a certain process have small
holes that render them unfit for use. Let X represent the number of containers in a
random sample of 10,000 that have this defect. Find P(X = 3).

Homework Equations


binomial?

The Attempt at a Solution



.97^9997
*
.03^3
*
10000 choose 3

i bet this actually does equal the answer maybe, but my calculator of course spits out 0 since .97^9997 is really really tiny...and doesn't take into account that 10000 choose 3 is humungo

help?
the answer is .224, but i can't match it
 
Physics news on Phys.org


It's not that hard to work it out "by hand".

[tex]\left(\begin{array}{c}10000 \\ 3\end{array}\right)= \frac{10000!}{3!9997!}[/tex]
[tex]= \frac{10000(9999)(9998)}{6}=\frac{10000}{2}\frac{9999}{3}(9998)[/tex]
[tex]= 166616670000[/tex]

Now, .033=0.000027 and (this is the hard one) .979997 is about .6 x 10-133. Since 166616670000 is about 1.7 x 1011 that product will be on the order of 1011- 5- 133= 10-127.

However, is the proportion of of "unfit" cups .03% or 3%? You say ".03%" but you use 3%. If the correct value is .03%, then you want
[tex]\left(\begin{array}{c}10000 \\ 3\end{array}\right)(.0003)^3)(.9997)^{9997}[/tex]
Now that would be about .224.
 
Last edited by a moderator:


i was sitting in class today and wondered if i can use poisson distribution on this?

o and yes that is my mistake that problem is copy and pasted, so yes it is actually 0.03% PERCENT...i didnt notice

edit: ah yes i figured it out, [tex]\lambda[/tex] = .0003*10000 = 3

P(X=3)

[tex]\frac{e^{-3}*3^{3}}{3!}[/tex] = .224

thanks a lot though
 
Last edited:

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 9 ·
Replies
9
Views
7K
  • · Replies 3 ·
Replies
3
Views
1K
  • · Replies 6 ·
Replies
6
Views
3K