Express the percent as a decimal 6.4%

Angel has an action figure collection of 300 action figures. He keeps 267 of the action figures on his wall. What percentage of

Veseljchak [2.6K]

Answer:

89% of Angel's action figure collection keeps on his wall.

Step-by-step explanation:

<u>Percentages</u>

To calculate the X% of a quantity Y, we must multiply both numbers and divide by 100:

X% of Y = X * Y / 100

For example, 15% of 780 = 15 * 780 / 100 = 117

If we want to know what percentage is m with respect to n, we proceed as follows:

percentage = m / n * 100%

Anges has a 300 action figures collection, 267 of which are kept on his wall. Here m=267, n=300, thus:

percentage = 267 / 300 * 100% = 89%

89% of Angel's action figure collection keeps on his wall.

5 0

3 years ago

Helpp me answer this question and explain

Ganezh [65]

The answer is A, you can tell because if you multiply 4 by the numbers inside the parentheses you’ll get =12+24..hope that helped :)

4 0

3 years ago

A population has a mean of 200 and a standard deviation of 50. Suppose a sample of size 100 is selected and x is used to estimat

zmey [24]

Answer:

a) 0.6426 = 64.26% probability that the sample mean will be within +/- 5 of the population mean.

b) 0.9544 = 95.44% probability that the sample mean will be within +/- 10 of the population mean.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

When the distribution is normal, we use 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 $s = \frac{\sigma}{\sqrt{n}}$ .