Question

Let x represent the dollar amount spent on supermarket impulse buying in a 10-minute (unplanned) shopping interval. Based on a certain article, the mean of the x distribution is about $46 and the estimated standard deviation is about $8.

(a) Consider a random sample of n = 40 customers, each of whom has 10 minutes of unplanned shopping time in a supermarket. From the central limit theorem, what can you say about the probability distribution of x-bar, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x-bar distribution?

The sampling distribution of x-bar is approximately normal with mean μ_x-bar = 46 and standard error σ_x-bar = $8.

The sampling distribution of x-bar is approximately normal with mean μ_x-bar = 46 and standard error σ_x-bar = $1.26.     

The sampling distribution of x-bar is not normal.

The sampling distribution of x-bar is approximately normal with mean μ_x-bar = 46 and standard error σ_x-bar = $0.20.

Is it necessary to make any assumption about the x distribution? Explain your answer.

It is not necessary to make any assumption about the x distribution because μ is large.

It is necessary to assume that x has a large distribution.     

It is necessary to assume that x has an approximately normal distribution.

It is not necessary to make any assumption about the x distribution because n is large.

(b) What is the probability that x-bar is between $44 and $48? (Round your answer to four decimal places.)


(c) Let us assume that x has a distribution that is approximately normal. What is the probability that x is between $44 and $48? (Round your answer to four decimal places.)


(d) In part (b), we used x-bar, the average amount spent, computed for 40 customers. In part (c), we used x, the amount spent by only one customer. The answers to parts (b) and (c) are very different. Why would this happen?

The x-bar distribution is approximately normal while the x distribution is not normal.

The standard deviation is larger for the x-bar distribution than it is for the x distribution.     

The mean is larger for the x-bar distribution than it is for the x distribution.

The sample size is smaller for the x-bar distribution than it is for the x distribution.

The standard deviation is smaller for the x-bar distribution than it is for the x distribution.

In this example, x-bar is a much more predictable or reliable statistic than x. Consider that almost all marketing strategies and sales pitches are designed for the average customer and not the individual customer. How does the central limit theorem tell us that the average customer is much more predictable than the individual customer?

The central limit theorem tells us that small sample sizes have small standard deviations on average. Thus, the average customer is more predictable than the individual customer.

The central limit theorem tells us that the standard deviation of the sample mean is much smaller than the population standard deviation. Thus, the average customer is more predictable than the individual customer.

Answer 1

(e)

The central limit theorem tells us that the standard deviation of the sample mean is much smaller than the population standard deviation. Thus, the average customer is more predictable than the individual customer.