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zheka24 [161]
2 years ago
13

Isaiah scores with 50% of his penalty kicks in soccer. He flips two fair coins to conduct a simulation with 20 trials to determi

ne the likelihood that he will make his next two penalty kicks, as shown. Heads up (H) represents a goal. What is the probability that Isaiah will make both penalty kicks? Give the probability as a percent. Enter your answer in the box.
Mathematics
1 answer:
MariettaO [177]2 years ago
8 0

Answer:

The probability that Isaiah will make both penalty kicks is 25%.

Step-by-step explanation:

We are given that Isaiah scores with 50% of his penalty kicks in soccer.

He flips two fair coins to conduct a simulation with 20 trials to determine the likelihood that he will make his next two penalty kicks.

The above situation can be represented through binomial distribution;

P(X =r) = \binom{n}{r} \times p^{r} \times (1-p)^{n-r};x=0,1,2,3,......

where, n = number of trials (samples) taken = 2 penalty kicks

           r = number of success = make both penalty kicks

           p = probability of success which in our question is probability  

                 that Isaiah scores with his penalty kicks, i.e; p = 50%

Let X = <u><em>Number of penalty kicks made by Isaiah</em></u>

So, X ~ Binom(n = 2 , p = 0.50)

Now, Probability that Isaiah will make both penalty kicks is given by = P(X = 2)

                     P(X = 2) =  \binom{2}{2} \times 0.50^{2} \times (1-0.50)^{2-2}

                                   =  1 \times 0.50^{2} \times 0.50^{0}

                                   =  <u>0.25 or 25%</u>

Hence, the probability that Isaiah will make both penalty kicks is 25%.

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Answer:

The probability that the mean amount of credit card debt in a sample of 1600 such households will be within $300 of the population mean is roughly 0.907 = 90.7%.

Step-by-step explanation:

To solve this question, we have to understand the normal probability distribution and the central limit theorem.

Normal probability distribution:

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore 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 pvalue, 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 random variable X, with mean \mu and standard deviation \sigma, a large sample size can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

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X = 1900

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

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X = 1300

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

Z = \frac{1300 - 1600}{178.125}

Z = -1.68

Z = -1.68 has a pvalue of 0.0465.

0.9535 - 0.0465 = 0.907.

The probability that the mean amount of credit card debt in a sample of 1600 such households will be within $300 of the population mean is roughly 0.907 = 90.7%.

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