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Fantom [35]
2 years ago
7

The number of hurricanes hitting the coast of Florida annually has a Poisson distribution with a mean of 0.8. Answer the followi

ng two questions: a) What is the probability that more than two hurricanes will hit the Florida coast in a year? b) What is the probability that exactly one hurricane will hit the coast of Florida in a year?
Mathematics
1 answer:
slava [35]2 years ago
3 0

Answer:

a) P(X>2)= 1-P(X \leq 2) = 1-[P(X=0)+P(X=1)+P(X=2)]

And we can find the individual probabilities like this:

P(X=0) = \frac{e^{-0.8} 0.8^0}{0!}= 0.4493

P(X=1) = \frac{e^{-0.8} 0.8^1}{1!}= 0.3595

P(X=2) = \frac{e^{-0.8} 0.8^2}{2!}= 0.1438

And replacing we got:

P(X>2)= 1-P(X \leq 2) = 1-[0.4493+0.3595+0.1438]=0.0474

b) P(X=1) = \frac{e^{-0.8} 0.8^1}{1!}= 0.3595

Step-by-step explanation:

Let X the random variable that represent the number of hurricanes hitting the coast of Florida annualle. We know that X \sim Poisson(\lambda=0.8)

The probability mass function for the random variable is given by:

f(x)=\frac{e^{-\lambda} \lambda^x}{x!} , x=0,1,2,3,4,...

And f(x)=0 for other case.

For this distribution the expected value is the same parameter \lambda=0.8

E(X)=\mu =\lambda=0.8

Part a

For this case we want this probability: P(X>2)

And for this case we can use the complement rule like this:

P(X>2)= 1-P(X \leq 2) = 1-[P(X=0)+P(X=1)+P(X=2)]

And we can find the individual probabilities like this:

P(X=0) = \frac{e^{-0.8} 0.8^0}{0!}= 0.4493

P(X=1) = \frac{e^{-0.8} 0.8^1}{1!}= 0.3595

P(X=2) = \frac{e^{-0.8} 0.8^2}{2!}= 0.1438

And replacing we got:

P(X>2)= 1-P(X \leq 2) = 1-[0.4493+0.3595+0.1438]=0.0474

Part b

Using the probability mass function we have:

P(X=1) = \frac{e^{-0.8} 0.8^1}{1!}= 0.3595

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Kitty [74]

Answer:

a

   The  90%  confidence interval is  52561.13  <  \mu  < 57540.8

b

Confidence interval for the population men between <u>$52561.13</u>  up to <u>$57540.8</u>

Step-by-step explanation:

From the question we are told that

   The sample size is  n = 25

     The  sample mean is  \= x  =  \$ 55,051

     The standard deviation is  \sigma  =  \$ 7,568

Given that the confidence level is  90% then the level of confidence is mathematically represented as

             \alpha  =  100 -90

              \alpha  = 10\%

             \alpha  = 0.10

Next we obtain the critical value of \frac{\alpha }{2} from the normal distribution table the values is  

               Z_{\frac{\alpha }{2} } =  1.645

Generally the margin of error is mathematically  represented as

               E =  Z_{\frac{ \alpha }{2} } * \frac{ \sigma }{\sqrt{n} }

substituting values

               E =  1.645 * \frac{ 7568}{ \sqrt{ 25} }

             E =  2489.9

The 90% confidence interval is mathematically evaluated as

       \= x  -E  <  \mu  <  \= x  +E

substituting values

     55051  - 2489.8  <  \mu  <  55051  + 2489.8

     52561.13  <  \mu  < 57540.8

6 0
2 years ago
A jar of 150 jelly beans contains 22 red jelly beans, 38 yellow, 20 green, 28 purple, 26 blue, and the rest are orange.Let B = t
pentagon [3]

Answer:

P(R\ and\ B) = 0.0256

Step-by-step explanation:

Given

Red = 22

Yellow = 38

Green = 20

Purple = 28

Blue = 26

Orange = 16

Required

Determine the probability of red then blue jelly? i.e. P(R and B)

From the question, we understand that the red jelly bean was not replaced. This means that the number of jelly beans reduced by 1 after the picking of the red jelly bean

So, we have:

P(R\ and\ B) = P(R)\ and\ P(B)

This is then solved further as:

P(R\ and\ B) = P(R)\ *\ P(B)

P(R\ and\ B) = \frac{n(R)}{Total}\ *\frac{n(B)}{Total - 1}

The probability has a denominator of Total - 1 because the number of jelly beans reduced by 1 after the picking of the red jelly bean

The equation becomes:

P(R\ and\ B) = \frac{22}{150}\ *\frac{26}{150- 1}

P(R\ and\ B) = \frac{22}{150}\ *\frac{26}{149}

P(R\ and\ B) = \frac{22*26}{150*149}

P(R\ and\ B) = \frac{572}{22350}

P(R\ and\ B) = 0.02559284116

P(R\ and\ B) = 0.0256

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The probability density function of the time to failure of an electronic component in a copier (in hours) is f(x) = (e^-x/1076)/
kozerog [31]

Answer:

(a) 0.06154

(b) 0.2389

(c) 0.6052

(d) 2478

Step-by-step explanation:

probability density function of the time to failure of an electronic component in a copier (in hours) is

P(x) = 1/1076e^−x/1076

λ = 1/1076

A) A component lasts more than 3000 hours before failure:

P(x>3000) = 1 − e^−3000/1076

= 0.06154

B) A component fails in the interval from 1000 to 2000 hours:

P(1000>x>2000) =1 − e^−2000/1076 − 1 +e^−1000/1076 = e^−1000/1076 − e^−2000/1076 = 0.3948 − 0.1559

= 0.2389

C) A component fails before 1000 hours:

P(x<1000) = 0.6052

D) The number of hours at which 10% of all components have failed:

e^−x/1076 = 0.1

= −x/1076

= ln(0.1)

x =(2.3026)×(1076)

x = 2478

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Answer:

Step-by-step explanation:

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