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Rama09 [41]
4 years ago
8

A peculiar die has the following properties: on any roll the probability of rolling either a 2, a 3, or a 5 is 1/2, just as it i

s with an ordinary die. Moreover, the probability of rolling either a 5, a 6, or a 4 is again 1/2. However, the probability of rolling a 5 is 3/16, not 1/6 as one would expect of an ordinary fair die.
From what you know about this peculiar die, compute
A) the probability of rolling either a 2, a 3, or a 1;
B) the probability of rolling anything but a 1.
Mathematics
1 answer:
igor_vitrenko [27]4 years ago
4 0

Answer:

A. P( 2∪3∪1 )=1/2

B. P( 2∪3∪4∪5∪6 )= 13/16

Step-by-step explanation:

A. At first we need to get all the information that we can from the question, so we focus on the probabilities given P(2∪3∪5)=1/2 and P(4∪5∪6)=1/2 and we need to use P(5)=3/16.

Notice that when you have a dice, it´s impossible to get two values at the same try. Because of this and the property of probability for events:

P(2∪3∪5)=P(2) + P(3) + P(5) - P(2∩3)(=0) - P(3∩5)(=0) - P(2∩5)(=0) + P(2∩3∩5)(=0)

P(2∪3∪5)=P(2) + P(3) + P(5)

1/2=P(2) + P(3) + 3/16

P(2)+P(3)=5/16

If we do the same with P(4∪5∪6), we get:

P(4)+P(6)=5/16

Now, with this new information we can find P(1):

P(1)= 1 - P(2∪3∪4∪5∪6)

P(1)= 1 - (P(2) + P(3) + P(4) + P(5) + P(6)) (The probability of two of them happening at the same time is zero)

P(1)= 1 - (5/16 + 5/16 + 3/16)

P(1)= 1 - 13/16

P(1)= 3/16

And with it, we can find P(2∪3∪1):

P(2∪3∪1)=P(2)+P(3)+P(1)

P(2∪3∪1)=5/16+3/16

P(2∪3∪1)=1/2

B. Notice if you want to roll anything but a 1, then the probability you are looking for is "P(2∪3∪4∪5∪6)" but we found that in part A

P(2∪3∪4∪5∪6)=13/16

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Step-by-step explanation:

remember, the number of possible combinations to pick m out of n elements is C(n, m) = n!/(m! × (n-m)!)

50,000 transistors.

4% are defective, that means 4/100 = 1/25 of the whole.

so, the probability for one picked transistor to be defective is 1/25.

and the probability for it to work properly is then 1-1/25 = 24/25.

now, 500 picks are done.

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the probability is then the sum of the probabilities to get

0 defective = (24/25)⁵⁰⁰

1 defective = (24/25)⁴⁹⁹×1/25 × C(500, 1)

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2 defective = (24/25)⁴⁹⁸×1/25² × C(500, 2)

= 24⁴⁹⁸/25⁵⁰⁰ × 250×499

3 defective = 24⁴⁹⁷/25⁵⁰⁰ × C(500, 3) =

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...

9 defective = 24⁴⁹¹/25⁵⁰⁰ × C(500, 9) =

= 24⁴⁹¹/25⁵⁰⁰ × 500×499×498×497×496×495×494×493×492×491 /

9×8×7×6×5×4×3×2 =

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best to use Excel or another form of spreadsheet to calculate all this and add it all up :

the probability that the engineer will accept the shipment is

0.004376634...

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is the acceptance policy good ?

that completely depends on the circumstances.

what was the requirement about max. faulty rate in the first place ? if it was 2%, then the engineer's approach is basically sound.

it then further depends what are the costs resulting from a faulty unit ? that depends again on when the defect is usually found (still in manufacturing, or already out there at the customer site, or somewhere in between) and how critical the product containing such transistors is. e.g. recalls for products are extremely costly, while simply sorting the bad transistors out during the manufacturing process can be rather cheap. if there is a reliable and quick process to do so.

so, depending on repair, outage and even penalty costs it might be even advisable to have a harder limit during the sample test.

in other words - it depends on experience and the found distribution/probability curve, standard deviation, costs involved and other factors to define the best criteria for the sample test.

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