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Alborosie
3 years ago
10

Judy washes 24 dishes. She then dries and stacks the dishes equally into 4 piles. How many dishes are in each pile?

Mathematics
2 answers:
deff fn [24]3 years ago
8 0


you are right it is  6 bcuz just use inverse opperations whats 24 divided by 4 which is 6 so 4 x 6= 24 so again u are right great job kid :p


Gnoma [55]3 years ago
5 0
Yes because the number 24/4=6
6*4=24
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<u>Answer:</u>

A) Scalene E) Obtuse


<u>Detailed Explanation:</u>


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Step-by-step explanation:

Hey there!

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<u>You can use the distance formula (d = rt) to solve this problem.  </u>

Downstream trip: d = 55 miles, t = 5 hours, and r = R + C  

R + C is their rowing speed (R) plus the rate of the current (C).  

Upstream trip: d = 55 miles, t = 11 hours, and r = R - C

R - C is their rowing speed (R) minus the rate of the current (C).  

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<u>Calculations</u>

R + C = 11 Subtract C from both sides.

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~I hope I helped you! :)~

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3 years ago
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At a new exhibit in the Museum of Science, people are asked to choose between 94 or 220 random draws from a machine. The machine
Tresset [83]

Answer:

0.0869 = 8.69% probability of getting more than 61% green balls.

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}}

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This means that p = \frac{99}{99+78} = 0.5593

Mean and standard deviation:

\mu = p = 0.5593

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.5593*0.4407}{99+78}} = 0.0373

a. Calculate the probability of getting more than 61% green balls.

This is 1 subtracted by the pvalue of Z when X = 0.61. So

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{0.61 - 0.5593}{0.0373}

Z = 1.36

Z = 1.36 has a pvalue of 0.9131

1 - 0.9131 = 0.0869

0.0869 = 8.69% probability of getting more than 61% green balls.

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