Qnt/561 Applied Business Research & Statistics
Applied Research and Statistics QNT561
Research and Sampling Designs
Shindeera Robinson
June 21, 2010
Chapter 8
21. What is sampling error? Could the value of the sampling error be zero? If it were zero, what would this mean?
Sampling error is the difference between the statistic estimated from a sample and the true population statistic. While we would expect the sampling error to not be zero, it is not impossible. For example if you were evaluating the ethnicities of a population and everyone in the population was Caucasian then taking any sample would give you the true proportion of 100% Caucasian. In other words, if the sampling error is zero then the population is uniform or you were taking a perfectly representative sample
22. List the reasons for sampling. Give an example of each reason for sampling.
A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents(people) selected from a larger population for the purpose of a survey. A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students
34. Information from the American Institute of Insurance indicates the mean amount of life insurance per household in the United States is $110,000. This distribution follows the normal distribution with a standard deviation of $40,000.
A. If we select a random sample of 50 households, what is the standard error of the mean?
U=110,000 S=40,000 N= 50
SE = s/ sqrt (n) = 40000/ sqrt (50) = 5656.85425
The standard error of the mean is 5656.85425
B. What is the expected shape of the distribution of the sample mean?
The expected shape of the sample mean will be the bell shaped curve, with the centered mean of 110,000 and a standard deviation of 5656.85
C. What is the likelihood of selecting a
Research and Sampling Designs
Shindeera Robinson
June 21, 2010
Chapter 8
21. What is sampling error? Could the value of the sampling error be zero? If it were zero, what would this mean?
Sampling error is the difference between the statistic estimated from a sample and the true population statistic. While we would expect the sampling error to not be zero, it is not impossible. For example if you were evaluating the ethnicities of a population and everyone in the population was Caucasian then taking any sample would give you the true proportion of 100% Caucasian. In other words, if the sampling error is zero then the population is uniform or you were taking a perfectly representative sample
22. List the reasons for sampling. Give an example of each reason for sampling.
A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents(people) selected from a larger population for the purpose of a survey. A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students
34. Information from the American Institute of Insurance indicates the mean amount of life insurance per household in the United States is $110,000. This distribution follows the normal distribution with a standard deviation of $40,000.
A. If we select a random sample of 50 households, what is the standard error of the mean?
U=110,000 S=40,000 N= 50
SE = s/ sqrt (n) = 40000/ sqrt (50) = 5656.85425
The standard error of the mean is 5656.85425
B. What is the expected shape of the distribution of the sample mean?
The expected shape of the sample mean will be the bell shaped curve, with the centered mean of 110,000 and a standard deviation of 5656.85
C. What is the likelihood of selecting a
References: Fridah, M. (n.d.). Sampling In Research. Retrieved June 21, 2010, from http://www.socialresearchmethods.net/tutorial/Mugo/tutorial.htm Lind, D. A. (2005). Statistical Techniques in Business & Economics (12 ed.). New York: The McGraw−Hill.