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Mathematics

1 answer:

antoniya [11.8K]3 years ago

5 0

I think the correct answer is -8x+4

explanation:
This would be considered a rise over run. So you see where the line hits the point on the y intercept (thats where the four came from) and you count down until you see where the point hits on the x intercept ( thats where the negative 8 came from) and you go over one because thats where the point was, so you can write -8/1 or you can just do -8x.

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What is the solution to the equation 37 x = 9 x + 4 ?<br> -7<br> − 1/7<br> 1/7<br> 7

natali 33 [55]

$▪▪▪▪▪▪▪▪▪▪▪▪▪ {\huge\mathfrak{Answer}}▪▪▪▪▪▪▪▪▪▪▪▪▪▪$

let's solve for x ~

$37x = 9x + 4$

$37x - 9x = 4$

$28x = 4$

$x = \dfrac{4}{28}$

$x = \dfrac{1}{7}$

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3 years ago

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You purchase pair of pants for $34.60 and a shirt for $12.30. If you need to pay 8% sales tax, what is the final price?

pantera1 [17]

Answer:

34.60+12.30=46.90

46.90/100=0.469

0.469*8=$3.75

7 0

4 years ago

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