What is 240 fl oz equal to in cups

1 answer:

7 0

What is 240 fl oz equal to in cups?

1 fl oz = 0.125 cups

Convert:-

240 × 0.125 = 30

<span>240 fl oz = 30 cups</span>

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Does anyone know how to do this ?

goldenfox [79]

Answer: D) 32

Step-by-step explanation:

The area of a parallelogram equals its base times its height. Substitute the base and area into this formula to solve for the height:

A=bh

40=(5)(h)

h=8

If ABCD is a parallelogram, opposite sides are parallel, so AB is parallel to DC. If AB is parallel to DC, EB is parallel to DC. Since EB is parallel to DC, quadrilateral EBCD has at least one pair of parallel sides, making it a trapezoid. The area of a trapezoid is equal to $\frac{h(b_{1}+b_{2}) }{2}$ where h is the height, $b_{1}$ is one base, and $b_{2}$ is the other base. Plug the necessary values into the formula:

A= $\frac{h(b_{1}+b_{2}) }{2}$

$b_{1} =AB-AE\\DC=AB\\b_{1} =DC-AE\\b_{1} =5-2\\b_{1} =3$

$b_{2} =5$

h=8

A= $\frac{8(3+5)}{2}$

A=32

The answer is D) 32.

6 0

2 years ago

How do I find this help please

KIM [24]

Area of the sector = 1/2 * r^2 * theta where theta is the angle subtended by the arc at the center ( in radians)

In this case m < theta = 360 - 235 = 125 degrees or 2.182 radians

So, the area of shaded area = 1/2 * 20^2 * 2.182 = 436.4 in^2

8 0

3 years ago

A manufacturer knows that their items have a normally distributed length, with a mean of 15.4 inches, and standard deviation of

Masteriza [31]

Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

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