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trapecia [35]
3 years ago
6

A card is drawn one at a time from a

Mathematics
1 answer:
dmitriy555 [2]3 years ago
6 0

Answer:

= \frac{6}{55}

Step-by-step explanation:

The computation of experimental probability is shown below:-

The Number of king in a well shuffled deck consists 52 cards which is

= 4

The Number of ways of drawing consists of 4 king in 13 repetitions which is

= ^{13}C_4

In 13 repetition, 2 kings are drawn by ^{13}C_2 way

Now,

P(E) = \frac{^{13}C_2}{^{13}C_4} = \frac{13 !} {(13-2) ! } / \frac{13 !}{(13 - 4)! 4!}

= \frac{13 !}{11 !\ 2 !} / \frac{13 !}{9 !\ 4 !}

= \frac{9 !\ 4 !}{11 !\ 2!}

= \frac{4\times 3}{11\times 10}

= \frac{6}{55}

Therefore for computing the experimental probability we simply applied the above formula.

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Step-by-step explanation:

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4 0
3 years ago
Read 2 more answers
HELP NEED ANSWER FOR HOMEWORK
Aleks04 [339]

Answer:

596.032

Step-by-step explanation:

The answer is typed wrong.

Using a calculator: 6.4 x 6.7 x 13.9 = 596.032

Tell your teacher that answer is not there. It's typed wrong.

Hoped this helped! :)

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
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