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vova2212 [387]
3 years ago
7

Three machines turn out all the products in a factory, with the first machine producing 30% of the products, the second machine

20%, and the third machine 50%. The first machine produces defective products 13% of the time, the second machine 9% of the time and the third machine 9% of the time. What is the probability that a non-defective product came from the second machine
Mathematics
1 answer:
kap26 [50]3 years ago
8 0

Answer:

0.2027 = 20.27% probability that a non-defective product came from the second machine

Step-by-step explanation:

Conditional Probability

We use the conditional probability formula to solve this question. It is

P(B|A) = \frac{P(A \cap B)}{P(A)}

In which

P(B|A) is the probability of event B happening, given that A happened.

P(A \cap B) is the probability of both A and B happening.

P(A) is the probability of A happening.

In this question:

Event A: Non-defective product.

Event B: From the second machine.

Probability of a non-defective product:

100-13 = 87% of 30%(first machine)

100-9 = 91% of 20%(second machine)

100-9 = 91% of 50%(third machine).

So

P(A) = 0.87*0.3 + 0.91*0.2 + 0.91*0.5 = 0.898

Non-defective and from the second machine:

91% of 20%. So

P(A \cap B) = 0.91*0.2 = 0.182

What is the probability that a non-defective product came from the second machine

P(B|A) = \frac{P(A \cap B)}{P(A)} = \frac{0.182}{0.898} = 0.2027

0.2027 = 20.27% probability that a non-defective product came from the second machine

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According to an NRF survey conducted by BIGresearch, the average family spends about $237 on electronics (computers, cell phones
Usimov [2.4K]

Answer:

(a) Probability that a family of a returning college student spend less than $150 on back-to-college electronics is 0.0537.

(b) Probability that a family of a returning college student spend more than $390 on back-to-college electronics is 0.0023.

(c) Probability that a family of a returning college student spend between $120 and $175 on back-to-college electronics is 0.1101.

Step-by-step explanation:

We are given that according to an NRF survey conducted by BIG research, the average family spends about $237 on electronics in back-to-college spending per student.

Suppose back-to-college family spending on electronics is normally distributed with a standard deviation of $54.

Let X = <u><em>back-to-college family spending on electronics</em></u>

SO, X ~ Normal(\mu=237,\sigma^{2} =54^{2})

The z score probability distribution for normal distribution is given by;

                                 Z  =  \frac{X-\mu}{\sigma}  ~ N(0,1)

where, \mu = population mean family spending = $237

           \sigma = standard deviation = $54

(a) Probability that a family of a returning college student spend less than $150 on back-to-college electronics is = P(X < $150)

        P(X < $150) = P( \frac{X-\mu}{\sigma} < \frac{150-237}{54} ) = P(Z < -1.61) = 1 - P(Z \leq 1.61)

                                                             = 1 - 0.9463 = <u>0.0537</u>

The above probability is calculated by looking at the value of x = 1.61 in the z table which has an area of 0.9463.

(b) Probability that a family of a returning college student spend more than $390 on back-to-college electronics is = P(X > $390)

        P(X > $390) = P( \frac{X-\mu}{\sigma} > \frac{390-237}{54} ) = P(Z > 2.83) = 1 - P(Z \leq 2.83)

                                                             = 1 - 0.9977 = <u>0.0023</u>

The above probability is calculated by looking at the value of x = 2.83 in the z table which has an area of 0.9977.

(c) Probability that a family of a returning college student spend between $120 and $175 on back-to-college electronics is given by = P($120 < X < $175)

     P($120 < X < $175) = P(X < $175) - P(X \leq $120)

     P(X < $175) = P( \frac{X-\mu}{\sigma} < \frac{175-237}{54} ) = P(Z < -1.15) = 1 - P(Z \leq 1.15)

                                                         = 1 - 0.8749 = 0.1251

     P(X < $120) = P( \frac{X-\mu}{\sigma} < \frac{120-237}{54} ) = P(Z < -2.17) = 1 - P(Z \leq 2.17)

                                                         = 1 - 0.9850 = 0.015

The above probability is calculated by looking at the value of x = 1.15 and x = 2.17 in the z table which has an area of 0.8749 and 0.9850 respectively.

Therefore, P($120 < X < $175) = 0.1251 - 0.015 = <u>0.1101</u>

5 0
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Which rate is equivalent to the unit rate of 13 miles per gallon? (5 points)
Sonbull [250]
13 miles per gallon....13/1
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26/2 = 13/1...they're equivalent...so ur answer is 26/2
4 0
4 years ago
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What is the solution of the compound inequality 2 &lt;2(x + 4) &lt; 18?
Nimfa-mama [501]
2<2x+8<18
-8     -8    -8
-6<2x<10
everything divides by two

the answer is  -3<x<5


...maybe lol i'm new
3 0
3 years ago
Computers from a certain manufacturer have a mean lifetime of 62 months, with a standard deviation of 12 months. The distributio
Goryan [66]

Answer:

Between 38 and 86 months.

Step-by-step explanation:

Chebyshev Theorem

The Chebyshev Theorem can also be applied to non-normal distribution. It states that:

At least 75% of the measures are within 2 standard deviations of the mean.

At least 89% of the measures are within 3 standard deviations of the mean.

An in general terms, the percentage of measures within k standard deviations of the mean is given by 100(1 - \frac{1}{k^{2}}).

In this question:

Mean of 62, standard deviation of 12.

Between what two lifetimes does Chebyshev's Theorem guarantee that we will find at least approximately 75% of the computers?

Within 2 standard deviations of the mean, so:

62 - 2*12 = 38

62 + 2*12 = 86

Between 38 and 86 months.

6 0
3 years ago
What fraction of a gallon is a quart
Papessa [141]
The answer is 1/4 since 4 quarts make 1 gallon.
3 0
3 years ago
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