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lys-0071 [83]
2 years ago
8

he life of light bulbs is distributed normally. The variance of the lifetime is 625 and the mean lifetime of a bulb is 540 hours

. Find the probability of a bulb lasting for at most 569 hours. Round your answer to four decimal places.
Mathematics
1 answer:
almond37 [142]2 years ago
8 0

Using the normal distribution, it is found that there is a 0.877 = 87.7% probability of a bulb lasting for at most 569 hours.

<h3>Normal Probability Distribution</h3>

The z-score of a measure X of a normally distributed variable with mean \mu and standard deviation \sigma is given by:

Z = \frac{X - \mu}{\sigma}

  • The z-score measures how many standard deviations the measure is above or below the mean.
  • Looking at the z-score table, the p-value associated with this z-score is found, which is the percentile of X.

The mean and the standard deviation are given, respectively, by:

\mu = 540, \sigma = \sqrt{625} = 25

The probability of a bulb lasting for at most 569 hours is the <u>p-value of Z when X = 569</u>, hence:

Z = \frac{X - \mu}{\sigma}

Z = \frac{569 - 540}{25}

Z = 1.16

Z = 1.16 has a p-value of 0.877.

0.877 = 87.7% probability of a bulb lasting for at most 569 hours.

More can be learned about the normal distribution at brainly.com/question/24663213

#SPJ1

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a) P(X=6)=(12C6)(0.54)^6 (1-0.54)^{12-6}=0.217  

b) P(X> 8) = P(X\geq 9)= P(X=9)+P(X=10)+P(X=11)+P(X=12)

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Step-by-step explanation:

Previous concepts  

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".  

Solution to the problem  

Let X the random variable of interest "number of teens that have heard of a fax machine", on this case we now that:  

X \sim Binom(n=12, p=0.54)  

The probability mass function for the Binomial distribution is given as:  

P(X)=(nCx)(p)^x (1-p)^{n-x}  

Where (nCx) means combinatory and it's given by this formula:  

nCx=\frac{n!}{(n-x)! x!}  

Part a

For this case we want this probability:

P(X=6)=(12C6)(0.54)^6 (1-0.54)^{12-6}=0.217  

Part b

For this case we want this probability:

P(X> 8) = P(X\geq 9)= P(X=9)+P(X=10)+P(X=11)+P(X=12)

P(X=9)=(12C9)(0.54)^9 (1-0.54)^{12-9}=0.0836  

P(X=10)=(12C10)(0.54)^{10} (1-0.54)^{12-10}=0.0294  

P(X=11)=(12C11)(0.54)^{11} (1-0.54)^{12-11}=0.00628  

P(X=12)=(12C12)(0.54)^{12} (1-0.54)^{12-12}=0.000615  

And adding the values we got:

P(X> 8) = P(X\geq 9)= P(X=9)+P(X=10)+P(X=11)+P(X=12)= 0.0836+0.0294+0.00628+0.000615 = 0.120

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