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valentinak56 [21]
3 years ago
8

Will give brainliest please explain in simple terms

Mathematics
1 answer:
Readme [11.4K]3 years ago
4 0

Answer:

25

Step-by-step explanation:

Hello, please consider the following.

\text{Let's note the 7 digits } x_i \text{ for }i = 0,1,2,3,4,5,6\\\\\text{We can write 7 equations as below.}\\\\(1) \ x_0+x_1+x_2+x_3=16\\\\(2) \ x_1+x_2+x_3+x_4=16\\\\(3) \ x_2+x_3+x_4+x_5=16\\\\(4) \ x_3+x_4+x_5+x_6=16\\\\(5) \ x_0+x_1+x_2+x_3+x_4=19\\\\(6) \ x_1+x_2+x_3+x_4+x_5=19\\\\(7) \ x_2+x_3+x_4+x_5+x_6=19

This is the translation in mathematical equations.

(5)-(1) gives x_4=19-16=3

(6)-(2) gives x_5=19-16=3

(7)-(3) gives x_6=19-16=3

So, by using (1)

x_0+x_1+x_2+x_3+x_4+x_5+x_6=(x_0+x_1+x_2+x_3)+3+3+3=16+9=25

Thank you

PS: If we go a little bit further it is not so hard to find that the number actually is 3337333

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The scores on the GMAT entrance exam at an MBA program in the Central Valley of California are normally distributed with a mean
Kaylis [27]

Answer:

58.32% probability that a randomly selected application will report a GMAT score of less than 600

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98.38% probability that a sample of 100 randomly selected applications will report an average GMAT score of less than 600

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 normally distributed samples are solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore 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 pvalue, 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.

In this problem, we have that:

\mu = 591, \sigma = 42

What is the probability that a randomly selected application will report a GMAT score of less than 600?

This is the pvalue of Z when X = 600. So

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

Z = \frac{600 - 591}{42}

Z = 0.21

Z = 0.21 has a pvalue of 0.5832

58.32% probability that a randomly selected application will report a GMAT score of less than 600

What is the probability that a sample of 50 randomly selected applications will report an average GMAT score of less than 600?

Now we have n = 50, s = \frac{42}{\sqrt{50}} = 5.94

This is the pvalue of Z when X = 600. So

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

Z = \frac{600 - 591}{5.94}

Z = 1.515

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What is the probability that a sample of 100 randomly selected applications will report an average GMAT score of less than 600?

Now we have n = 50, s = \frac{42}{\sqrt{100}} = 4.2

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

Z = \frac{600 - 591}{4.2}

Z = 2.14

Z = 2.14 has a pvalue of 0.9838

98.38% probability that a sample of 100 randomly selected applications will report an average GMAT score of less than 600

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rodikova [14]
Answer is 12.73
Use Pythagorean theory
Split square into two right triangles with diagonal line. To find the diagonal
a^2 + b^2 = c^2
9^2 + 9^2 = c^2
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Take square root of both sides
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