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nekit [7.7K]
2 years ago
10

One of the wagers in roulette is to bet that the ball will stop on a number that is a multiple of 3. (Both 0 and 00 are not incl

uded.) If the ball stops on such a number, the player wins double the amount bet. If a player bets $5, compute the player's expectation. (Round your answer to two decimal places.)
Mathematics
1 answer:
goldenfox [79]2 years ago
4 0

Answer:

The player's expectation is a loss of 20 cents.

Step-by-step explanation:

In a game of roulette, there are 38 slots where the ball can stop.

The slots are numbered as follows: {0, 00, 1, 2, 3 ..., 36}.

So, N = 38.

The slots which are multiples of 3 are:

S = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33 and 36}

So, n (S) = 12.

Let <em>X</em> = the ball stops on a number that is a multiple of 3

The probability that the ball stops on a number that is a multiple of 3 is:

P(X)=\frac{n (S)}{N}=\frac{12}{38}=0.32

It s provided that the player wins double the amount bet if the ball stops on a number that is a multiple of 3.

Compute the player's expectation as follows:

E(X)=\sum X\cdot P(X)

        =(\$10\times 0.32)+(-\$5\times (1-0.32))\\\\=\$3.20-\$3.40\\\\=-\$0.20

Thus, the player's expectation is a loss of 20 cents.

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Calculate the value of x to one decimal place. inches
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Answer:

Step-by-step explanation:

Hello!

For me, the first step to any statistics exercise is to determine what is the variable of interest and it's distribution.

In this example the variable is:

X: height of a college student. (cm)

There is no information about the variable distribution. To estimate the population mean you need a variable with at least a normal distribution since the mean is a parameter of it.

The option you have is to apply the Central Limit Theorem.

The central limit theorem states that if you have a population with probability function f(X;μ,δ²) from which a random sample of size n is selected. Then the distribution of the sample mean tends to the normal distribution with mean μ and variance δ²/n when the sample size tends to infinity.

As a rule, a sample of size greater than or equal to 30 is considered sufficient to apply the theorem and use the approximation.

The sample size in this exercise is n=50 so we can apply the theorem and approximate the distribution of the sample mean to normal:

X[bar]~~N(μ;σ2/n)

Thanks to this approximation you can use an approximation of the standard normal to calculate the confidence interval:

98% CI

1 - α: 0.98

⇒α: 0.02

α/2: 0.01

Z_{1-\alpha /2}= Z_{1-0.01}= Z_{0.99} =2.334

X[bar] ± Z_{1-\alpha /2} * \frac{S}{\sqrt{n} }

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With a confidence level of 98%, you'd expect that the true average height of college students will be contained in the interval [172.22; 176.78].

I hope it helps!

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