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loris [4]
8 months ago
5

suppose a production line stops for maintenance whenever a defective product is produced. if there is a 1% chance of a defective

product, what is an upper bound on the probability that at least 175 products are made before a defect is discovered? (hint: find an upper bound, not the actual probability.
Mathematics
1 answer:
Mariana [72]8 months ago
4 0

An upper bound on the probability that at least 175 products are made before a defect is discovered is 0.57 (Option C)

According to Markov's inequality, the chance that a positive real number is larger than or equal to a given positive random variable X is either less than or equal to the expected value of X divided by a.

Let X stand for the random variable that represents the first flaw found; X has a geometric distribution with p=0.01.

Then we'll have: E(X) = (1 - p) / p = (1 - 0.01) / 0.01 = 99

Markov's inequality has been used to create:

P(X ≥ 175) = E(X) / 175 = 99 / 175 = 0.5657 ≅ 0.57

Therefore, the highest limit on the likelihood that at least 175 goods be produced before a flaw is found is:

P(X ≥ 175) = 0.57

Therefore, option C is the correct choice.

To know more about Markov's inequality, refer to this link:

brainly.com/question/28902943

#SPJ4

<u>COMPLETE QUESTION:</u>

Suppose a production line stops maintenance whenever a defective product is produced. If there is a 1% chance of a defective product, what is an upper bound on the probability that at least 175 products are made before a defect is discovered? (Hint: Find an upper bound, NOT the actual probability.)

a. 0.34

b. 0.01

c. 0.57

d. 0.17

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Estimate the product 5.25 × 8.89.
Anon25 [30]

Answer:

47.25

Step-by-step explanation:

8.89 is about 9.

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1 year ago
An article suggested that yield strength (ksi) for A36 grade steel is normally distributed with μ = 42 and σ = 5.5.
alexandr1967 [171]

Answer:

a)P( X

We want this probability:

P( X >64)

And using the z score formula given by:

z = \frac{x -\mu}{\sigma}

We got:

P( X >64) =P(Z> \frac{64-42}{5.5}) =P(Z>4)=0.0000316

b) For this part we want to find a value a, such that we satisfy this condition:

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P(X   (b)

Both conditions are equivalent on this case. We can use the z score again in order to find the value a.  

As we can see on the figure attached the z value that satisfy the condition with 0.75 of the area on the left and 0.25 of the area on the right it's z=0.674. On this case P(Z<0.674)=0.75 and P(z>0.674)=0.25

If we use condition (b) from previous we have this:

P(X  

P(z

But we know which value of z satisfy the previous equation so then we can do this:

z=0.674

And if we solve for a we got

a=42 +0.674*5.5=45.707

So the value of height that separates the bottom 75% of data from the top 25% is 45.707.  

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Part a

Let X the random variable that represent the heights of a population, and for this case we know the distribution for X is given by:

X \sim N(42,25.5)  

Where \mu=42 and \sigma=5.5

And we want this probability:

P( X

And using the z score formula given by:

z = \frac{x -\mu}{\sigma}

We got:

P( X

We want this probability:

P( X >64)

And using the z score formula given by:

z = \frac{x -\mu}{\sigma}

We got:

P( X >64) =P(Z> \frac{64-42}{5.5}) =P(Z>4)=0.0000316

Part b

For this part we want to find a value a, such that we satisfy this condition:

P(X>a)=0.25   (a)

P(X   (b)

Both conditions are equivalent on this case. We can use the z score again in order to find the value a.  

As we can see on the figure attached the z value that satisfy the condition with 0.75 of the area on the left and 0.25 of the area on the right it's z=0.674. On this case P(Z<0.674)=0.75 and P(z>0.674)=0.25

If we use condition (b) from previous we have this:

P(X  

P(z

But we know which value of z satisfy the previous equation so then we can do this:

z=0.674

And if we solve for a we got

a=42 +0.674*5.5=45.707

So the value of height that separates the bottom 75% of data from the top 25% is 45.707.  

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