The average income in a certain region in 2013 was $68 comma 000 per person per year. Suppose the standard deviation is $28 comma 000 and the distribution is right-skewed. Suppose we take a random sample of 100 residents of the region. a. Is the sample size large enough to use the Central Limit Theorem for means? Explain. b. What are the mean and standard error of the sampling distribution? c. What is the probability that the sample mean will be more than $2 comma 800 away from the population mean?

Using the normal distribution and the central limit theorem, we have that:

a) The sample size is of 100, which is larger than 30, hence it is large enough to use the Central Limit Theorem for means.

b) The mean is of $68,000 while the standard deviation is of $2,800.

c) There is a 0.3174 = 31.74% probability that the sample mean will be more than $2,800 away from the population mean.

In a normal distribution with mean and standard deviation , the z-score of a measure X is given by:

In this problem:

Item a:

The sample size is of 100, which is larger than 30, hence it is large enough to use the Central Limit Theorem for means.

Item b:

The mean is of $68,000 while the standard deviation is of $2,800.

Item c:

First, we find the z-score, which is:

The probability is P(|Z| > 1), which is 2 multiplied by the p-value of Z = -1.

Looking at the z-table, Z = -1 has a p-value of 0.1587

2 x 0.1587 = 0.3174.

There is a 0.3174 = 31.74% probability that the sample mean will be more than $2,800 away from the population mean.

A similar problem is given at link

(a) Yes, the sample size large enough to use the Central Limit Theorem for means.

(b) The mean and standard error of the sampling distribution are $68,000 and $2,800 respectively.

(c) The probability that the sample mean is more than $2800 away from the population mean is 0.1587.

Step-by-step explanation:

According to the Central Limit Theorem if we have a population with mean μ and standard-deviation σ and appropriately huge random samples (n > 30) are selected from the population with replacement, then the distribution of the sample mean will be approximately normally distributed.

Then, the mean of the distribution of sample means is given by,

And the standard deviation of the distribution of sample means is given by,

This is also known as the standard error.

Let X = income in a certain region in 2013.

The mean and standard deviation of the random variable X is:

A random sample of size, n = 100 residents are selected from the region.

(a)

The sample selected is quite large, i.e. n = 100 > 30.

The Central limit theorem can be applied to approximate the distribution of the sample mean income in that region.

(b)

Compute the mean of the sampling distribution of sample mean as follows:

Compute the standard error of the sampling distribution of sample mean as follows:

Thus, the mean and standard error of the sampling distribution are $68,000 and $2,800 respectively.

(c)

Compute the probability that the sample mean is more than $2800 away from the population mean as follows:

Thus, the probability that the sample mean is more than $2800 away from the population mean is 0.1587.

binomial distribution

median= 2

range = 7-0 = 7

how to find median?

no. of term=28

median=(28+1)/2=14.5th term

the 14th and 15th term are 2, therefore the median is (2+2)/2 = 2

the factors of 24 are 1, 2, 3, 4, 6, 8, 12 and 24. hope this ! mark brainliest! you v much! : )