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Gala2k [10]
3 years ago
15

The probability a randomly selected household owns corporate stock is 0.54. The probability a randomly selected household has no

Internet access is 0.3. Assume whether a randomly selected household owns corporate stock is independent of whether the household has no Internet access. What is the probability a randomly selected household has no Internet access given the household owns corporate stock
Mathematics
1 answer:
34kurt3 years ago
8 0

Answer:

30% probability a randomly selected household has no Internet access given the household owns corporate stock

Step-by-step explanation:

I am going to say that we have two events.

Event A: Owning corporate stock. So P(A) = 0.54.

Event B: Having no internet access. So P(B) = 0.3.

Since they are independent events, we can apply the conditional probability formula, which is:

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

In which

P(B|A) is the probabilitty of event B happening given that A happened. We want to find this.

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

Since they are independent

P(A \cap B) = P(A)P(B) = 0.54*0.3

So

P(B|A) = \frac{P(A \cap B)}{P(A)} = \frac{0.54*0.3}{0.54} = 0.3

30% probability a randomly selected household has no Internet access given the household owns corporate stock

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Answer:

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Explanation:

Work backward:

1. 2x + 23 = 30

Start:

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Multiply the equation by 2:

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Subtract 30 from both sides

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2. 3x + 19.5 = 30

Start:

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Add 6.5 to both sides:

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Multiply by 3:

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Distributive property:

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3. (10x - 20)/ 5 = 3

Start:

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Answer:

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At a new exhibit in the Museum of Science, people are asked to choose between 94 or 220 random draws from a machine. The machine
Tresset [83]

Answer:

0.0869 = 8.69% probability of getting more than 61% green balls.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score of a measure X is given by:

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

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean \mu = p and standard deviation s = \sqrt{\frac{p(1-p)}{n}}

The machine is known to have 99 green balls and 78 red balls.

This means that p = \frac{99}{99+78} = 0.5593

Mean and standard deviation:

\mu = p = 0.5593

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.5593*0.4407}{99+78}} = 0.0373

a. Calculate the probability of getting more than 61% green balls.

This is 1 subtracted by the pvalue of Z when X = 0.61. So

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

By the Central Limit Theorem

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

Z = \frac{0.61 - 0.5593}{0.0373}

Z = 1.36

Z = 1.36 has a pvalue of 0.9131

1 - 0.9131 = 0.0869

0.0869 = 8.69% probability of getting more than 61% green balls.

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