In a shipment of onions, 12% are damaged. 1,144 onions are undamaged.

1 answer:

5 0

Since 12% of them are damaged, that means the percentage of them that aren't 88%.
The easiest way to do it is to divide 88 from 1,144, written as 1,144<span>÷88.
That's 13. So, then you would multiply 13 by 100. To get the total amount of onions. That'd be 1,300.</span>

You might be interested in

Find the average rate of change of the function f(x), represented by the graph, over the interval [0, 3]. Calculate the average

Over [174]

Answer:

[f(3) - f(0)]/(3-0)

[f(3) - f(0)]/3

7 0

3 years ago

4b - 7 - 15b + 3<br> Pls give full explanation I don't understand this stuff.

allsm [11]

Combine like terms so add 4b and 15b together and add 7 and 3 together and then the answer would be 19b-10

5 0

3 years ago

Read 2 more answers

SEE PHOTO

Xelga [282]

Answer:

b-m b-c b-r

Step-by-step explanation:

8 0

3 years ago

Read 2 more answers

What are two other names for Xy

NARA [144]

Answer:

In Mathematics, x and y are examples of variables.

You can use any letter as a variable but, make sure to include a let statement when using variables.

A let statement essentially tells what the variable represents.

Ex: let h = houses sold

Hope this helps!

Step-by-step explanation:

3 0

3 years ago

Read 2 more answers

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

algol13

Answer:

The probability that the sample mean would be at least $39000 is of 0.8665 = 86.65%.

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