STAT101: Spring 2012 - Midterm 2 Review Problems: Probability and Defective Items, Exams of Statistics

Additional problems for the stat101: spring 2012 midterm 2 exam, focusing on probability theory and defective items in a production line. The problems include calculating the probability of certain events based on given conditions and identifying the appropriate distribution for predicting the number of defective items.

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2011/2012

Uploaded on 05/18/2012

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STAT101: Spring 2012
More Mideterm 2 Review Problems
Brian Powers TA
Here's a few more problems โ€“ these may be a little more challenging but they will help prepare for the
exam.
1. If
P๎‚žB๎‚Ÿ=0.60
and
P๎‚žAโˆฉB๎‚Ÿ=0.10
, which is more likely: A|B or B|A?
2. If it rains there is a 70% chance that it is windy also. The forecast gives a 25% chance of rain.
Therefore, the probability of wind is
.70โ‹….25=.175
(ie 17.5%) probability of wind.
What is wrong with this reasoning?
3. At a certain factory, units are produced on the assembly line one after the other. Sometimes there is a
glitch in the system and the factory produces defective items.
A defective item is produced after a working item with probability 0.1%. A defective item is followed
by another defective item with probability 95%. Assume that the machines are checked every night so
that the first item of the day is a working item with 100% probability.
โ€ขWhat is the probability that the second item of the day is working?
โ€ขWhat is the probability that the second through 5th items are all working?
โ€ขWhat is the probability that the second item is defective by the 10 after it are all working?
โ€ขWhat is the probability that the first 4 items of the day will be working, defective,
working, and working (in that order)?
โ€ขIf item #3 is defective, what is the probability that item #2 was working?
โ€ขIf the company produces 50 items today, what is the probability that the company will
only produce 1 defective item today?
โ€ขIs the binomial distribution appropriate to predict the # of defective items produced? Why
or why not?
pf3
pf4

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STAT101: Spring 2012 More Mideterm 2 Review Problems Brian Powers TA Here's a few more problems โ€“ these may be a little more challenging but they will help prepare for the exam.

  1. If P ๎‚ž B ๎‚Ÿ=0.60 and P ๎‚ž A โˆฉ B ๎‚Ÿ=0.10 , which is more likely: A|B or B|A?
  2. If it rains there is a 70% chance that it is windy also. The forecast gives a 25% chance of rain. Therefore, the probability of wind is .70โ‹….25=.175 (ie 17.5%) probability of wind. What is wrong with this reasoning?
  3. At a certain factory, units are produced on the assembly line one after the other. Sometimes there is a glitch in the system and the factory produces defective items. A defective item is produced after a working item with probability 0.1%. A defective item is followed by another defective item with probability 95%. Assume that the machines are checked every night so that the first item of the day is a working item with 100% probability.
    • What is the probability that the second item of the day is working?
    • What is the probability that the second through 5th^ items are all working?
    • What is the probability that the second item is defective by the 10 after it are all working?
    • What is the probability that the first 4 items of the day will be working, defective, working, and working (in that order)?
    • If item #3 is defective, what is the probability that item #2 was working?
    • If the company produces 50 items today, what is the probability that the company will only produce 1 defective item today?
    • Is the binomial distribution appropriate to predict the # of defective items produced? Why or why not?

STAT101: Spring 2012 More Mideterm 2 Review Problems Brian Powers TA Here's a few more problems โ€“ these may be a little more challenging but they will help prepare for the exam.

  1. If P ๎‚ž B ๎‚Ÿ=0.60 and P ๎‚ž A โˆฉ B ๎‚Ÿ=0.10 , which is more likely: A|B or B|A? Solution: P ๎‚ž A โˆฃ B ๎‚Ÿ=

P ๎‚ž A โˆฉ B ๎‚Ÿ

P ๎‚ž B ๎‚Ÿ

โ‰ˆ.1667 (^) and P ๎‚ž B โˆฃ A ๎‚Ÿ=

P ๎‚ž B โˆฉ A ๎‚Ÿ

P ๎‚ž A ๎‚Ÿ

P ๎‚ž A ๎‚Ÿ

but P(A) is unknown. What do we know about P(A)? We can re-write the probability of A as the probability of A and B plus the probability of A and NOT B: P ๎‚ž A ๎‚Ÿ= P ๎‚ž A โˆฉ B ๎‚Ÿ๎‚ƒ P ๎‚ž A โˆฉ B C ๎‚Ÿ=.10๎‚ƒ P ๎‚ž A โˆฉ B C ๎‚Ÿ Because P ๎‚ž^ B ๎‚Ÿ=.60^ , P ๎‚ž BC^ ๎‚Ÿ=.40 , so we know that P ๎‚ž^ A ๎‚Ÿโ‰ค.10๎‚ƒ. Thus: P ๎‚ž^ A ๎‚Ÿโ‰ค.50^ so

P ๎‚ž A ๎‚Ÿ

โ‰ฅ (^2) , and

P ๎‚ž A ๎‚Ÿ

P ๎‚ž A ๎‚Ÿ

Basically, we know that because P(A) is no bigger than .5, P(B|A) must be larger than. Therefore, we can say with certainty that P(B|A) > P(A|B)

  1. If it rains there is a 70% chance that it is windy also. The forecast gives a 25% chance of rain. Therefore, the probability of wind is .70โ‹….25=.175 (ie 17.5%) probability of wind. What is wrong with this reasoning? Solution: The problem with the reasoning is that windy with no rain is being completely ignored. We can say the prbability of wind AND rain is 17.5%, but P ๎‚ž Wind ๎‚Ÿ= P ๎‚ž Wind โˆฉ Rain ๎‚Ÿ๎‚ƒ P ๎‚ž Wind โˆฉ No Rain ๎‚Ÿ.
  2. At a certain factory, units are produced on the assembly line one after the other. Sometimes there is a glitch in the system and the factory produces defective items. A defective item is produced after a working item with probability 0.1%. A defective item is followed by another defective item with probability 95%. Assume that the machines are checked every night so that the first item of the day is a working item with 100% probability.
    • What is the probability that the second item of the day is working? We know the first item is working from the problem, so we can base this off the probability that an item works given the previous item was working P ๎‚ž Item 2 isWorking ๎‚Ÿ= P ๎‚ž Working Item โˆฃ Previous Item was Working ๎‚Ÿ=99.
      • What is the probability that the second through 5th^ items are all working? We want to know the probability of 4 working items in a row given the first item was working. The probability the second works is.

You can see that the probability of item #3 being defective is the same โ€“ you will take the product of the exact same numbers. That is also true up through item #49 being defective The exception is if item #50 is the defective item. In this case the probability is .999^48 โ‹….001โ‰ˆ. So by adding up all of these probabilities we have: P ๎‚ž 1 out of 50 is defective ๎‚Ÿ= 48 ร—.0000477๎‚ƒ.000953โ‰ˆ.

  • Is the binomial distribution appropriate to predict the # of defective items produced? Why or why not? Absolutely not! Because the probability of producing a defective item depends on the item before it, these are not independent Bernoulli trials, so we cannot use a Binomial model for this problem.