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 xdistribution is about $26 and the estimated standard deviation is about $7.

(a) Consider a random sample of n = 60 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution?

-The sampling distribution of x is approximately normal with mean μ_x = 26 and standard error σ_x = $7.

The sampling distribution of x is not normal.     

-The sampling distribution of x is approximately normal with mean μ_x = 26 and standard error σ_x = $0.90.

-The sampling distribution of x is approximately normal with mean μ_x = 26 and standard error σ_x = $0.12.

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 n is large.I

-t is necessary to assume that x has a large distribution.     

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

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

(b) What is the probability that x is between $24 and $28? (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 $24 and $28? (Round your answer to four decimal places.)


(d) In part (b), we used x, the average amount spent, computed for 60 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 standard deviation is larger for the x distribution than it is for the x distribution.

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

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

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

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

In this example, x 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 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.

-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.    

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 $34 and the estimated standard deviation is about $9. (a) Consider a random sample of n = 110 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution? The sampling distribution of x is not normal. The sampling distribution of x is approximately normal with mean μx = 34 and standard error σx = $0.08. The sampling distribution of x is approximately normal with mean μx = 34 and standard error σx = $0.86. The sampling distribution of x is approximately normal with mean μx = 34 and standard error σx = $9. Is it necessary to make any assumption about the x distribution? Explain your answer. It is necessary to assume that x has an approximately normal distribution. It is necessary to assume that x has a large distribution. It is not necessary to make any assumption about the x distribution because μ is large. It is not necessary to make any assumption about the x distribution because n is large. (b) What is the probability that x is between $32 and $36? (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 $32 and $36? (Round your answer to four decimal places.) (d) In part (b), we used x, the average amount spent, computed for 110 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 distribution is approximately normal while the x distribution is not normal. The sample size is smaller for the x distribution than it is for the x distribution. The standard deviation is larger for the x distribution than it is for the x distribution. The mean is larger for the x distribution than it is for the x distribution. The standard deviation is smaller for the x distribution than it is for the x distribution. In this example, x 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 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. 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.

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 $39 and the estimated standard deviation is about $7.

(a) Consider a random sample of n = 60 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution?

The sampling distribution of x is approximately normal with mean μ_x = 39 and standard error σ_x = $0.90.The sampling distribution of x is approximately normal with mean μ_x = 39 and standard error σ_x = $7.    The sampling distribution of x is approximately normal with mean μ_x = 39 and standard error σ_x = $0.12.The sampling distribution of x is not normal.

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

It is necessary to assume that x has a large distribution.It is not necessary to make any assumption about the x distribution because n is large.    It is not necessary to make any assumption about the x distribution because μ is large.It is necessary to assume that x has an approximately normal distribution.

(b) What is the probability that x is between $37 and $41? (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 $37 and $41? (Round your answer to four decimal places.)


(d) In part (b), we used x, the average amount spent, computed for 60 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 standard deviation is larger for the x distribution than it is for the x distribution.The mean is larger for the x distribution than it is for the x distribution.    The x distribution is approximately normal while the x distribution is not normal.The standard deviation is smaller for the x distribution than it is for the x distribution.The sample size is smaller for the x distribution than it is for the x distribution.

In this example, x 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.

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 $31 and the estimated standard deviation is about $8. (a) Consider a random sample of n = 70 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution? The sampling distribution of x is not normal. The sampling distribution of x is approximately normal with mean μx = 31 and standard error σx = $0.11. The sampling distribution of x is approximately normal with mean μx = 31 and standard error σx = $8. The sampling distribution of x is approximately normal with mean μx = 31 and standard error σx = $0.96. 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 is between $29 and $33? (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 $29 and $33? (Round your answer to four decimal places.) (d) In part (b), we used x, the average amount spent, computed for 70 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 standard deviation is smaller for the x distribution than it is for the x distribution. The x distribution is approximately normal while the x distribution is not normal. The sample size is smaller for the x distribution than it is for the x distribution. The mean is larger for the x distribution than it is for the x distribution. The standard deviation is larger for the x distribution than it is for the x distribution. In this example, x 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.

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 $47 and the estimated standard deviation is about $8.

(a) Consider a random sample of n = 80 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution?

The sampling distribution of x is approximately normal with mean μ_x = 47 and standard error σ_x = $0.89.The sampling distribution of x is not normal.    The sampling distribution of x is approximately normal with mean μ_x = 47 and standard error σ_x = $0.10.The sampling distribution of x is approximately normal with mean μ_x = 47 and standard error σ_x = $8.

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

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 μ is large.   

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

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

(b) What is the probability that x is between $45 and $49? (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 $45 and $49? (Round your answer to four decimal places.)


(d) In part (b), we used x, the average amount spent, computed for 80 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 distribution is approximately normal while the x distribution is not normal.

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

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

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

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

In this example, x 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.    

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 $49 and the estimated standard deviation is about $8.

(a) Consider a random sample of n = 60 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, the average amount spent by these customers due to impulse buying? What are the mean and standard deviation of the x distribution?

The sampling distribution of x is not normal.
The sampling distribution of x is approximately normal with mean μ_x = 49 and standard error σ_x = $8.    
The sampling distribution of x is approximately normal with mean μ_x = 49 and standard error σ_x = $0.13.
The sampling distribution of x is approximately normal with mean μ_x = 49 and standard error σ_x = $1.03.

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 an approximately normal distribution.    
It is not necessary to make any assumption about the x distribution because n is large.
It is necessary to assume that x has a large distribution.

(b) What is the probability that x is between $47 and $51? (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 $47 and $51? (Round your answer to four decimal places.)


(d) In part (b), we used x, the average amount spent, computed for 60 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 sample size is smaller for the x distribution than it is for the x distribution.
The standard deviation is smaller for the x distribution than it is for the x distribution.    
The x distribution is approximately normal while the x distribution is not normal.
The mean is larger for the x distribution than it is for the x distribution.
The standard deviation is larger for the x distribution than it is for the x distribution.

In this example, x 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.    

From a sample of 36 graduate​ students, the mean number of months of work experience prior to entering an MBA program was 36.95. The national standard deviation is known to be 19 months. What is a 95​% confidence interval for the population​ mean? A 95​% confidence interval for the population mean is left bracket nothing comma nothing right bracket . ​(Use ascending order. Round to two decimal places as​ needed.)

Just to get us started on this discussion, can anyone discuss the differences between a manufacturing company versus a merchandising company? And beyond that, how is a service company different from both of these? Also, if you can think of any examples of any one of these types of companies that would be good to add to your posting.

The nurse is admitting a 68-year-old patient with a history of ovarian cancer to the medical unit. She had surgery 2 months ago and has had pain ever since the surgery. She reports that she has been taking oxycodone at home, but that the pain is “never gone”

4-During the evening rounds, the patient is founded to be unresponsive with respiratory rate of 7 breath/min. Her son, who was staying with her, said that he “pushed the button a few times” while she was asleep because earlier “she said she was hurting but wouldn’t push it herself”. What would be the priority nursing actions?

Ariana contributed $100 from her pay cheque at the beginning of every month from age 18 to 65 into an RRSP account (no contribution in the month of her 65th birthday). Macy contributed $4000 at the beginning of every year from age 35 to 55 into a similar RRSP account, then leave the money in the fund to accumulate for another 10 years(no contribution in the year of her 55th birthday). money earned 4.8% compounded daily in both RRSP accounts.
1. who had the greater accumulates value, and how much were they.
2. how much interest did each earn