What is 8tens subtracted from 97 ones?

A shelf will covered with veneer on all 6 sides. The shelf is 48 inches long, 12 inches wide and 1 inch thick. What is the surfa

Liono4ka [1.6K]

So. hmmmm notice the first picture, that's a rectangular prism

is really just 6 rectangles stacked up to each other at the edges

now, notice the second picture, is a prism/shelf with a length of 48, width of 12 and height of 1

so, your shelf is really just 6 rectangles stacked up to each other at the edges

you have 2 rectangles of 48x1 on the sides
2 rectangles of 12x1 for the front and back
and 2 rectangles of 12x48, for the top and bottom

get the area of each of those rectangles, add them up, and that's the surface area of the shelf

6 0

3 years ago

6. Cynthia walks 6 miles at a rate of 2 miles

Stolb23 [73]

3 hours is the correct answer

5 0

3 years ago

Read 2 more answers

A coffee machine dispenses normally distributed amounts of coffee with a mean of 12 ounces and a standard deviation of 0.2 ounce

NemiM [27]

Answer:

0.9332 = 93.32% probability that the mean of the sample will be less than 12.1 ounces.

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