Purchasing something you don’t need means?

Find the equation of the line

kotegsom [21]

Of the line of what?

7 0

2 years ago

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Six more than five times a number (x) is at least twenty-one

I am Lyosha [343]

The answer to this would be 5x+6>=21. Just wanted points :P

3 0

3 years ago

A theme park company is opening a marine-inspired park in your city. They are in the process of designing the theatre where a ki

leva [86]

The formula for the volume of a quarter-sphere is simply one-fourth of the volume of the sphere. So the volume of a quarter-sphere:
V = (1/4) (4/3) (π r³)
or
V = (1/3) (π r³)

Since we are given with the radius of 70 feet, the volume is
V = (1/3) (π) (70³)
V = 359,189 cubic feet

3 0

3 years ago

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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

93.51% probability that a sample of 50 randomly selected applications will report an average GMAT score of less than 600

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