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vodomira [7]
1 year ago
6

Suppose we roll one fair six-sided die, and flip six coins. what is the probability that the number of heads is equal to the num

ber showing on the die?
Mathematics
1 answer:
sergejj [24]1 year ago
3 0

The probability that the number of heads is equal to the number showing on the die is \frac{21}{128} .

According to the question

we roll one fair six-sided die, and flip six coins

i.e

The Total Outcomes is the product of the sample spaces for the dice and the coins.  

sample spaces for the dice = 6

sample spaces for the coins = 2⁶

S = 6.2⁶

S = 384

Now, The  probability is

Therefore, the Favorable Outcomes

Consider the number of ways to get each total of heads from 1 to 6.

= 2⁶-1 (as TTT is excluded )

= 63

The probability that the number of heads is equal to the number showing on the die

As

Probability can be defined as the ratio of the number of favorable outcomes to the total number of outcomes of an event.

Probability(Event) = Favorable Outcomes/Total Outcomes

By substituting the values in the formula

Probability(Event) = \frac{63}{384}

Probability(Event) = \frac{21}{128}

Hence, the probability that the number of heads is equal to the number showing on the die is \frac{21}{128}

To know more about probability here:

brainly.com/question/11234923

#SPJ4

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The remaining part of Question:

4) what can we conclude about sampling variability in the sample proportion calculated in the sample at John Hopkins as compared to that calculated in the sample at Ohio State.

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Answer:

4) The sample proportion from Johns Hopkins will have about the same sampling variability as that from Ohio State

5) The sample proportion from John Hopkins will have more sampling variability than that from Ohio State

Step-by-step explanation:

Note: The sampling variability in the sample proportion decreases with increase in the sample size.

4) since the sample size at both Johns Hopkins and Ohio State is the same (i.e. n = 50), the sample variability of the sample proportion will be the same for both cases.

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At Johns Hopkins, sample size, n = 3% * 2000

n = 60

At Ohio State, sample size, n = 3% * 40000

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Since sampling variability in the sample proportion decreases with increase in the sample size, the sampling variability in sample proportion will be higher at Johns Hopkins than at Ohio State.

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Answer:

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Step-by-step explanation:

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Step-by-step explanation:

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