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3 years ago

Ilus

Mathematics

1 answer:

geniusboy [140]3 years ago

8 0

Answer:

<h2> $\bold{B.\quad \ y^3-\dfrac R2}$ </h2>

Step-by-step explanation:

"half of R" it is: $\dfrac R2$

"y cubed" it is: $y^3$

Therefore, half of R subtracted from y cubed is: $y^3-\dfrac R2$

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The answer is the letter A! I hope this helps

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3 years ago

April took out a $600 loan from the bank.At the end of 5 years, she pays back the principle,plus $60 simple interest.What was th

Vedmedyk [2.9K]

Answer:

April took out a loan of $600 and paid it back with simple interest of $60 after 5 years. The formula to calculate interest is given the principal and the time and the interest rate is . For this problem we have to find the interest rate given . To archive that , we can just solve the equation making the interest rate the subject of the formula as shown below,

I= Prt

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3 years ago

on monday, roger drove to work in 20 minutes. on tuesday, he averaged 15 miles per hour more, and it took him 6 minutes less to

ch4aika [34]

Let the distance to his office be x, then
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4 years ago

Lesson Check (4.MD.C.7)

vredina [299]

The measure of Zx is 4

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3 years ago

it is known that the population proton of utha residnet that are members of the church of jesus christ 0l6 suppose a random samp

Lady_Fox [76]

Answer:

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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 $s = \sqrt{\frac{p(1-p)}{n}}$