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Grace [21]
3 years ago
13

Which of the following is a solution of y > |x| - 5? a.(4, -1) B.(-1, -4) C.(-4, 1)

Mathematics
1 answer:
Savatey [412]3 years ago
8 0
Fill your inequality in with the y and x provided and then do the math.  (4, -1) would fill in like this (I will use brackets to indicate absolute value symbols, since there are none in the equation editor): -1\ \textgreater \ [4]-5  The right side is in fact equal to the left side so that's not the answer.  For (-1, -4): -4\ \textgreater \ [-1]-5 and these are also equal.  Let's try C now (-4, 1): 1\ \textgreater \ [-4]-5.  The absolute vale of -4 is 4 so 4 - 5 = -1 which is, in fact, less than 1.  So C is our answer.
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A statistician calculates that 8% of Americans own a Rolls Royce. If the statistician is right, what is the probability that the
hichkok12 [17]

Answer:

0.007 = 0.7% probability that the proportion of Rolls Royce owners in a sample of 595 Americans would differ from the population proportion by more than 3%

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 establishes 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}}

A statistician calculates that 8% of Americans own a Rolls Royce.

This means that p = 0.08

Sample of 595:

This means that n = 595

Mean and standard deviation:

\mu = p = 0.08

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.08*0.92}{595}} = 0.0111

What is the probability that the proportion of Rolls Royce owners in a sample of 595 Americans would differ from the population proportion by more than 3%?

Proportion above 8% + 3% = 11% or below 8% - 3% = 5%. Since the normal distribution is symmetric, these probabilities are equal, and so we find one of them and multiply by 2.

Probability the proportion is less than 5%:

P-value of Z when X = 0.05. So

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

By the Central Limit Theorem

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

Z = \frac{0.05 - 0.08}{0.0111}

Z = -2.7

Z = -2.7 has a p-value of 0.0035

2*0.0035 = 0.0070

0.007 = 0.7% probability that the proportion of Rolls Royce owners in a sample of 595 Americans would differ from the population proportion by more than 3%

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Answer:

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Slope of perpendicular: -1/2

Pick a point for y =2x + 7. For example (0,7)

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Distance formula \sqrt{(x_{2}-x_{1} )^{2}+(y_{2} -y_{1})^2   }

Insert (0,7) and (4,5)

\sqrt{(0-4)^{2}+(7-5)^2   } = \sqrt{(-4)^{2}+(2)^2   } = \sqrt{(16)+(4)}  } = \sqrt{20}  = 2\sqrt{5}

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