What Proportion of Pins Produced Will Be Unusable?

  • Thread starter semidevil
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In summary, the problem involves creating a pin that is 6 cm long, but the machine produces pins that are 6 + y cm long, where 0 <= y <= 2. The function f(y) = K(y + y^2) represents the proportion of unusable pins. To find the proportion of produced pins that are unusable, the integral of f(y) must be taken from 1 to 2 and subtracted from 1. This requires finding the value of K by normalizing the probability distribution. The final answer is 23/28.
  • #1
semidevil
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I don't remember the exact story problem, but it goes something like this.

You are suppose to make a pin that is 6 cm long. Unfortunately, the machine will produce pins that 6 + y cm long. where 0 <= y <= 2, and the function f(y) = K(y + y^2).

if the pin is more then 7 cm long, then it is unusable. Find the proportion that is produced that will be unusable.

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ok, so if y = 0, or y = 1, then it is fine, and if y = 2, then it is unusable.

so can I just integrate from 0 to 1 of f(y), and then subtract from 1, to get the answer?

the book says the answer is 23/28...but I don't know how it got that.
 
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  • #2
semidevil said:
ok, so if y = 0, or y = 1, then it is fine, and if y = 2, then it is unusable.

Not quite. It should read: If 0<=y<=1, then it is fine, and if 1<y<=2, then it is unusable.

so can I just integrate from 0 to 1 of f(y), and then subtract from 1, to get the answer?

Yes, but first, you have to find K. To get it you have to normalize the probability distribution by integrating f(y) over the sample space and setting it equal to 1. This will allow you to solve for K. Then you can do what you have proposed above.
 
  • #3


Yes, you can integrate from 0 to 1 of f(y) and subtract it from 1 to find the proportion that is usable. This is because f(y) represents the probability that the pin will be longer than 7 cm, and we want to find the proportion that is shorter than 7 cm.

To find the proportion that is unusable, we can simply subtract the proportion that is usable from 1. So the equation would be:

1 - ∫(0 to 1) f(y)dy = 1 - ∫(0 to 1) K(y + y^2)dy

= 1 - [Ky + (Ky^2)/2] evaluated at y = 0 and y = 1

= 1 - [K(1) + (K(1)^2)/2] + [K(0) + (K(0)^2)/2]

= 1 - [K + K/2] + 0

= 1 - 3K/2

We know that y can range from 0 to 2, so we need to find the value of K that would make the integral of f(y) from 0 to 2 equal to 1.

∫(0 to 2) K(y + y^2)dy = 1

= 2K + (4K)/3

= (10K)/3

So, (10K)/3 = 1

K = 3/10

Substituting this value of K into our equation for unusable proportion, we get:

1 - 3(3/10)/2

= 1 - 9/20

= 11/20

Therefore, the proportion of pins that will be unusable is 11/20 or 0.55. This is not the same as the book's answer of 23/28, so it's possible that there was a mistake in the book's solution or in the given information.
 

1. What is the definition of probability?

Probability is a measure of the likelihood that an event will occur. It is typically expressed as a fraction or decimal between 0 and 1, where 0 indicates impossibility and 1 indicates certainty.

2. How is probability calculated?

Probability is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This is known as the classical definition of probability and assumes that all outcomes are equally likely.

3. What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data collected from experiments or observations, and may differ from theoretical probability due to chance or bias.

4. How does probability relate to statistics?

Probability is a fundamental concept in statistics, as it is used to determine the likelihood of events occurring and to make predictions based on data. Statistics also helps to identify and analyze patterns in data, which can inform probability calculations.

5. What are some real-world applications of probability?

Probability is used in a wide range of fields, including finance, insurance, weather forecasting, and sports. It can be used to analyze risk, make predictions, and inform decision-making in various industries.

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