Solve the problems. <br> (will give brainliest)

THANK YOU , PLEASE INCLUDE EXPLANATION

frosja888 [35]

Remark

This will be a little late, but I'll answer it anyway. The more you see of the sine law, the clearer it will be.

Step One

Set up the sine Law.

$\dfrac{Sin(x)}{12}=\dfrac{sin(75)}{22}$

Step Two

Substitute values

Sin(x) = ??

sin(75) = 0.9659

$\dfrac{Sin(x)}{12}=\dfrac{0.9659}{22}$

Step Three

Cross multiply and Solve

22* Sin(x) = 12 * 0.9659

22* Sin(x) = 11.591 divide by 22

sin(x) = 11.591 / 22

sin(x) = 0.5269

Step Four

You have to know how to use the inverse of an angle to get the answer. I do it as my calculator would do it. If it doesn't work with yours, let me know.

x = sin^-1(0.5269)

2nd F

Sin^-1

(

0.5269

)

=

You should get 31.79 degrees.

5 0

3 years ago

a beach ball has a radius of 8 inches. find how many cubic inches to the nearest 100th of air was used to fill the beach ball

viktelen [127]

Answer:

800 i think

Step-by-step explanation:

6 0

3 years ago

Based on historical data, your manager believes that 37% of the company's orders come from first-time customers. A random sample

fomenos

Answer:

0.6214 = 62.14% probability that the sample proportion is between 0.26 and 0.38

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

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