The greatest number of cubical blocks, 4 inches on each edge, that can be used to fill a cubical box, 2 ft on each edge, is

Bingel [31]

5 is you answer because absolute value is the distance on a number line from 0

8 0

3 years ago

The value of a stock over a 12 year period form 2003 to 2015 is shown in the line graph. Which statement is not supported by the

lukranit [14]

The correct answer is the last one listed: the statement not supported by the graph is that a stock purchased in 2006 has not yet recovered the losses from 2008. That is not true.

3 0

3 years ago

Read 2 more answers

Plz get correct for brainiest

Scorpion4ik [409]

Answer:

well all you need to do is try your best

Step-by-step explanation:

3 0

3 years ago

What is the value of x in the figure?<br><br> Enter your answer in the box

Tcecarenko [31]

Answer:

x =68

Step-by-step explanation:

We know that x+22 = 90 because the sum of the two angles is a vertical angle to the right angle and vertical angles are equal.

x+22 =90

Subtract 22 from each side

x+22-22 = 90-22

x =68

3 0

3 years ago

Annual starting salaries in a certain region of the U. S. for college graduates with an engineering major are normally distribut

defon

Answer:

0.8665 = 86.65% probability that the sample mean would be at least $39000

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