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podryga [215]
3 years ago
5

Lucia and Marcus are playing a board game. They roll 2 dice (number cubes) to see who goes first. The person who rolls the highe

st sum gets to go first. If there is a tie, they each roll the dice again. Lucia went first and she rolled a 4 and a 1. Find the probability that Marcus will not get to go immediately after he rolls the dice. Show all of your work below. Express your answer as a fraction in simplest form, a decimal and a percent.
15points
Mathematics
1 answer:
yaroslaw [1]3 years ago
6 0

Ans

Step-by-step explanation:

                                   

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m_a_m_a [10]

Answer:

Let X the random variable who represents the file sizeof music. We know the following info:

\mu =2.3,\sigma =3.25

We select a sample of n=50 nails. That represent the sample size.  

Since the sample size is large enough n >30, we can use the central limit theorem. From this theorem we know that the distribution for the sample mean \bar X is given by:

\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})

And we can approximate the distribution of the sample mean as a normal distribution and no matter if the distribution for X is right skewed or no.

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".  

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

Let X the random variable who represents the file sizeof music. We know the following info:

\mu =2.3,\sigma =3.25

We select a sample of n=50 nails. That represent the sample size.  

Since the sample size is large enough n >30, we can use the central limit theorem. From this theorem we know that the distribution for the sample mean \bar X is given by:

\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})

And we can approximate the distribution of the sample mean as a normal distribution and no matter if the distribution for X is right skewed or no.

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Answer:

thats way to much

Step-by-step explanation:

OK

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This is equivalent to:

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yanalaym [24]

Answer:

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Step-by-step explanation:

\frac{4 x 5}{2}

4 x 5 = 20

\frac{20}{2} = 10

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2 years ago
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