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ss7ja [257]
3 years ago
5

How to change this fraction into a decimal by dividing ?

Mathematics
1 answer:
hoa [83]3 years ago
7 0
You would divide 4 by 33 to get 0.12
bar notation or the little line on top saying its repeating. Hope I helped :)
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Solve for x.<br> x = [?]<br> 4x - 2<br> 3x + 14
Ilya [14]

Answer:

X = 7

Step-by-step explanation:

I really don’t know

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3 years ago
Item 5 A farmer plants corn and wheat on a 180-acre farm. The farmer wants to plant three times as many acres of corn as wheat.
valina [46]

Answer:


Step-by-step explanation:

180/4 is 45 so you could say the farmer plants 135 Acers of corn and only 45 of wheat

3x+y=180  

Hope this helped.

8 0
3 years ago
Make 24 using 1,2,2,7
alexdok [17]
7 times 2=14
14 times 2=24
24 times 1=24
5 0
3 years ago
Read 2 more answers
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}}

The machine is known to have 99 green balls and 78 red balls.

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.

8 0
3 years ago
PLEASE HELP QUICK!!! 30 PTS
kotegsom [21]
He would earn <span>$40.95 in that amount of time


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3 years ago
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