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Svetradugi [14.3K]
1 year ago
12

The Breaker's Manufacturing Company makes 80% of a particular sensor, the Cartin Company makes 15% of them, and the Flutes Compa

ny makes the other 5%. The sensors made by Breaker's have a 4% defect rate, the Cartin sensors have a 6% defect rate, and the Flutes sensors have a 9% defect rate. If a sensor is randomly selected from the general population of all sensors, what is the probability that it is defective, given that it was made by Breaker’s?
a: 0.04
b: 0.05
c: 0.15
d: 0.80
Mathematics
1 answer:
Mice21 [21]1 year ago
8 0
The defect rate of a company is equal to the probability of a randomly selected sensor from that company being defective. 

Breaker's has a 4% defect rate, so the probability that a Breaker's sensor being defective is 4%, or 0.04. 

The answer is A) 0.04
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4. Hydrocarbons in the cab of an automobile were measured during trips on the New Jersey Turnpike and trips through the Lincoln
pochemuha

Answer:

No these these result do not differ at 95% confidence level  

Step-by-step explanation:

From the question we are told that

  The first concentrations is  c _1= 30.0 \ g/m^3

      The second concentrations is  c _2 = 52.9 \ g/m^3

  The first sample size is  n_1 =  32

    The second sample size is  n_2 =  32

   The  first standard deviation is \sigma_1 =  30.0

     The  first standard deviation is \sigma_1 =  29.0

The mean for  Turnpike is  \= x _1 = \frac{c_1}{n}  =  \frac{31.4}{32} = 0.98125

The mean for   Tunnel is  \= x _2 = \frac{c_2}{n}  =  \frac{52.9}{32} = 1.6531

The  null hypothesis is  H_o  :  \mu _1 - \mu_2  =  0

The  alternative hypothesis is  H_a  :  \mu _1 - \mu_2  \ne  0

Generally the test statistics is mathematically represented as

              t =  \frac{\= x_1 - \= x_2}{ \sqrt{\frac{\sigma_1^2}{n_1}  +\frac{\sigma_2^2}{n_2} }}

         t =  \frac{0.98125 - 1.6531}{ \sqrt{\frac{30^2}{32}  +\frac{29^2}{32} }}

        t = - 0.0899

Generally the degree of freedom is mathematically represented as

     df =  32+ 32 - 2

     df =  62

The  significance \alpha  is  evaluated as

      \alpha  =  (C - 100 )\%

=>   \alpha  =  (95 - 100 )\%

=>   \alpha  =0.05

The  critical value  is evaluated as

      t_c  =  2  *  t_{0.05 ,  62}

From the student t- distribution table  

        t_{0.05, 62} =  1.67

So

     t_c  =  2 * 1.67

=>  t_c  = 3.34

given that

       t_c  >  t we fail to reject the null hypothesis so  this mean that the result do  not differ

       

6 0
2 years ago
Bora is planning to save money to buy a computer for his daughter. He already has $552.50, and he can save $65 each month. The c
melomori [17]

Answer:

yeh

Step-by-step explanation:

that's all right.

1105=65m+552.5

is correct.

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2 years ago
Payton leaves to go on an
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2 years ago
Customers are used to evaluate a preliminary product design. In the past, 95% of highly successful products received good review
Sever21 [200]

Answer:

a. 61.5%; b. About 61.8%; c. About 36.4%

Step-by-step explanation:

This is a kind of question that we can solve using the Bayes' Theorem. We have here all the different conditional probabilities we need to solve this problem.

According to that theorem, the probability of a selected product attains a good review is:

\\ P(G) = P(G|H)*P(H) + P(G|M)*P(M) + P(G|P)*P(P) (1)

In words, the probability that a selected product attains a <em>good review</em> is an <em>event </em>that depends upon the sum of the conditional probabilities that the product comes from <em>high successful product</em> P(G|H) by the probability that this product is a <em>highly successful product</em> P(H), plus the same about the rest of the probabilities, that is, P(G|M)*P(M) or the probability that the product has a good review coming from a <em>moderately successful</em> product by the probability of being moderately successful, and a good review coming from a poor successful product by the probability of being poor successful or P(G|P)*P(P).

<h3>The probability that a randomly selected product attains a good review</h3>

In this way, the probability that a randomly selected product attains a good review is the result of the formula (1). Where (from the question):

P(G|H) = 95% or 0.95 (probability of receiving a good review being a highly successful product)

P(G|M) = 60% or 0.60 (probability of receiving a good review being a moderately successful product)

P(G|P) = 10% or 0.10 (probability of receiving a good review being a poorly successful product)

P(H) = 40% or 0.40 (probability of  being a highly successful product).

P(M) = 35% or 0.35 (probability of  being a moderately successful product).

P(P) = 25% or 0.25 (probability of  being a poor successful product).

Then,

\\ P(G) = P(G|H)*P(H) + P(G|M)*P(M) + P(G|P)*P(P)

\\ P(G) = 0.95*0.40 + 0.60*0.35 + 0.10*0.25

\\ P(G) = 0.615\;or\; 61.5\%

That is, <em>the probability that a randomly selected product attains a good review</em> is 61.5%.

<h3>The probability that a new product attains a good review is a highly successful product</h3>

We are looking here for P(H|G). We can express this probability mathematically as follows (another conditional probability):

\\ P(H|G) = \frac{P(G|H)*P(H)}{P(G)}

We can notice that the probability represents a fraction from the probability P(G) already calculated. Then,

\\ P(H|G) = \frac{0.95*0.40}{0.615}

\\ P(H|G) =\frac{0.38}{0.615}

\\ P(H|G) =0.618

Then, the probability of a product that attains a good review is indeed a highly successful product is about 0.618 or 61.8%.

<h3>The probability that a product that <em>does not attain </em>a good review is a moderately successful product</h3>

The probability that a product does not attain a good review is given by a similar formula than (1). However, this probability is the complement of P(G). Mathematically:

\\ P(NG) = P(NG|H)*P(H) + P(NG|M)*P(M) + P(NG|P)*P(P)

P(NG|H) = 1 - P(G|H) = 1 - 0.95 = 0.05

P(NG|M) = 1 - P(G|M) = 1 - 0.60 = 0.40

P(NG|P) = 1 - P(G|M) = 1 - 0.10 = 0.90

So

\\ P(NG) = 0.05*0.40 + 0.40*0.35 + 0.90*0.25

\\ P(NG) = 0.385\;or\; 38.5\%

Which is equal to

P(NG) = 1 - P(G) = 1 - 0.615 = 0.385

Well, having all this information at hand:

\\ P(M|NG) = \frac{P(NG|M)*P(M)}{P(NG)}

\\ P(M|NG) = \frac{0.40*0.35}{0.385}

\\ P(M|NG) = \frac{0.14}{0.385}

\\ P(M|NG) = 0.363636... \approx 0.364

Then, the <em>probability that a new product does not attain a good review and it is a moderately successful product is about </em>0.364 or 36.4%.

8 0
2 years ago
Chris built a rectangular snow fort with a perimeter of 24 feet. The length of the fort was 8 feet less than 3 time the width. W
Ira Lisetskai [31]
24= x+y
X= 3y-8
24= 3y-8+y
24=4y-8
32=4y
Y=8
X=16
4 0
1 year ago
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