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2 years ago

Please help ASAP I will give brainliest if you answer right

Mathematics

2 answers:

EastWind [94]2 years ago

5 0

Answer:

J) 15

Step-by-step explanation:

Difference means subtraction:

10 - (-5)

10 + 5

Leto [7]2 years ago

4 0

Distance = subtraction
10 - (-5) = 10 + 5 = 15

The distance is 15

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Answer:

1) 6x = 21

2) x + y - 3

3) x/z = y

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1. The product of a number x and 6 is 21

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We then have a product of x and 6, that x * 6, which we write usually in the format 6x.

is 21: that means it's equal to 21....

so 6x = 21.

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The sum is an addition. The sum of a and b is a + b.

In this case, the first part is x - 3, the second part is y

So, x - 3 + y, which we usually rewrite as x + y - 3

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3 years ago

Annual starting salaries in a certain region of the U. S. for college graduates with an engineering major are normally distribut

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Answer:

0.8665 = 86.65% probability that the sample mean would be at least $39000

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 $s = \frac{\sigma}{\sqrt{n}}$ .