Question 1

The difference between the point estimate and the true value of the population parameter being estimated is called the estimation error.

Accepted Answer

A) True 
 B)False

Question 2

The primary advantage of cluster sampling is sampling convenience (and possibly less cost). The downside, however, is that the inferences drawn from a cluster sample can be less accurate, for a given sample size, than for other sampling plans.

Accepted Answer

A) True 
 B)False

Question 3

The probability of being chosen in a simple random sample of size n from a population of size N is:

Accepted Answer

A)  1/N 
B)  N - 1/n 
C)  N/n 
D)  n/N 
A)  1/N 
B)  N - 1/n 
C)  N/n 
D)  n/N

Question 4

Systematic sampling is generally similar to simple random sampling in its statistical properties.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following statements are correct?

Accepted Answer

A)  A point estimate is an estimate of the range of a population parameter 
B)  A point estimate is a single value estimate of the value of a population parameter 
C)  A point estimate is an unbiased estimator if its standard deviation is the same as the actual value of the population standard deviation 
D)  All of these choices are correct 
A)  A point estimate is an estimate of the range of a population parameter 
B)  A point estimate is a single value estimate of the value of a population parameter 
C)  A point estimate is an unbiased estimator if its standard deviation is the same as the actual value of the population standard deviation 
D)  All of these choices are correct

Question 6

The approximate 95% confidence interval for a population mean is:

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)

Question 7

The standard error of an estimate is the standard deviation of the sampling distribution of the estimate. It measures how much estimates from different samples vary.

Accepted Answer

A) True 
 B)False

Question 8

It is customary to approximate the standard error of the sample mean   by substituting the sample standard deviation s for   in the formula: SE(   ) =   .

Accepted Answer

A) True 
 B)False

Question 9

An unbiased estimate is a point estimate such that the mean of its sampling distribution is equal to the true value of the population parameter being estimated.

Accepted Answer

A) True 
 B)False

Question 10

The standard error of sample mean   is large when the observations in the population are spread out (large   ), but that the standard error can be reduced by taking a smaller sample.

Accepted Answer

A) True 
 B)False

Question 11

A statistics professor has just given the final examination in his introductory statistics course. In particular, he is interested in learning how his class of 50 students performed on this exam. The data are shown below.  
-(A) Technically, this process does not yield a random sample of the books in the store. Why not?
(B) How could a truly random sample be obtained?

Accepted Answer

(A) For true random sampling, all possib

Question 12

The sampling mean   is the ____ estimate for the population mean   .

Accepted Answer

A)  random 
B)  point 
C)  simple 
D)  interval 
A)  random 
B)  point 
C)  simple 
D)  interval

Question 13

The standard deviation of   is usually called the:

Accepted Answer

A)  standard error of the mean 
B)  standard error of the sample 
C)  standard error of the population 
D)  randomized standard error 
A)  standard error of the mean 
B)  standard error of the sample 
C)  standard error of the population 
D)  randomized standard error

Question 14

The theorem that states that the sampling distribution of the sample mean   is approximately normal when the sample size n is reasonably large is known as the:

Accepted Answer

A)  central limit theorem 
B)  central tendency theorem 
C)  simple random sample theorem 
D)  point estimate theorem 
A)  central limit theorem 
B)  central tendency theorem 
C)  simple random sample theorem 
D)  point estimate theorem

Question 15

Which of the following statements is correct?

Accepted Answer

A)  A confidence interval describes a range of values that is likely not to include the actual population parameter 
B)  A confidence interval is an estimate of the range for a sample statistic. 
C)  A confidence interval is an estimate of the range of possible values for a population parameter. 
D)  None of these statements is correct. 
A)  A confidence interval describes a range of values that is likely not to include the actual population parameter 
B)  A confidence interval is an estimate of the range for a sample statistic. 
C)  A confidence interval is an estimate of the range of possible values for a population parameter. 
D)  None of these statements is correct.

Question 16

The key to using stratified sampling is:

Accepted Answer

A)  identifying the strata 
B)  selecting the appropriate strata 
C)  defining the strata 
D)  randomizing the strata 
A)  identifying the strata 
B)  selecting the appropriate strata 
C)  defining the strata 
D)  randomizing the strata

Question 17

The finite population correction factor is a correction for the standard error when the sample size is fairly large relative to the population size.

Accepted Answer

A) True 
 B)False

Question 18

A statistics professor has just given the final examination in his introductory statistics course. In particular, he is interested in learning how his class of 50 students performed on this exam. The data are shown below.  
-(A) Using these 50 students as the frame, use Excel to generate a simple random sample of size 10 from this frame.
​
(B) Compute the mean scores in the frame and the simple random sample you generated in (A).
​
(C) Compare the mean scores you computed in (B). Is your simple random sample a good representative of the frame? Why or why not?
​
(D) Using these 50 students as the frame, use Excel to generate a systematic sample of size 10 from this frame.
​
(E) Compare the mean scores in the frame with that in the systematic sample in (D). What do you conclude?

Accepted Answer

​
In order to solve this problem, we fir

Question 19

Sally Bird of Big Rapids Realty has received data on 60 houses that were recently sold in Mecosta County in Michigan. The data are recorded in the table shown below. Included in this data set are observations for each of the following variables:
· The appraised value of each house (in thousands of dollars)
· The selling price of each house (in thousands of dollars)
· The size of each house (in hundreds of square feet)
· The number of bedrooms in each house  
-(A) Suppose that Sally wishes to examine a representative subset of these 60 houses that has been stratified by the number of bedrooms. Use Excel to assist her by finding such a stratified sample of size 10 with proportional sample sizes.
​
(B) Explain how Sally could apply cluster sampling in selecting a sample of size 15 from this frame.
​
(C) What are the advantages and disadvantages of employing cluster sampling in this case?

Accepted Answer

​
(A) In this problem, the stratified sa

Question 20

A sampling error is the result of:

Accepted Answer

A)  measurement error 
B)  nonresponse bias 
C)  nontruthful responses 
D)  "unlucky" sampling 
A)  measurement error 
B)  nonresponse bias 
C)  nontruthful responses 
D)  "unlucky" sampling

The difference between the point estimate and the true value of the population parameter being estimated is called the estimation error.

The primary advantage of cluster sampling is sampling convenience (and possibly less cost). The downside, however, is that the inferences drawn from a cluster sample can be less accurate, for a given sample size, than for other sampling plans.

The probability of being chosen in a simple random sample of size n from a population of size N is:

Systematic sampling is generally similar to simple random sampling in its statistical properties.

Which of the following statements are correct?

The approximate 95% confidence interval for a population mean is:

The standard error of an estimate is the standard deviation of the sampling distribution of the estimate. It measures how much estimates from different samples vary.

It is customary to approximate the standard error of the sample mean by substituting the sample standard deviation s for in the formula: SE( ) = .

An unbiased estimate is a point estimate such that the mean of its sampling distribution is equal to the true value of the population parameter being estimated.

The standard error of sample mean is large when the observations in the population are spread out (large ), but that the standard error can be reduced by taking a smaller sample.

The sampling mean is the ____ estimate for the population mean .

The standard deviation of is usually called the:

The theorem that states that the sampling distribution of the sample mean is approximately normal when the sample size n is reasonably large is known as the:

Which of the following statements is correct?

The key to using stratified sampling is:

The finite population correction factor is a correction for the standard error when the sample size is fairly large relative to the population size.

A sampling error is the result of:

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 7: Sampling and Sampling Distributions

The difference between the point estimate and the true value of the population parameter being estimated is called the estimation error.

The primary advantage of cluster sampling is sampling convenience (and possibly less cost). The downside, however, is that the inferences drawn from a cluster sample can be less accurate, for a given sample size, than for other sampling plans.

The probability of being chosen in a simple random sample of size n from a population of size N is:

Systematic sampling is generally similar to simple random sampling in its statistical properties.

Which of the following statements are correct?

The approximate 95% confidence interval for a population mean is:

The standard error of an estimate is the standard deviation of the sampling distribution of the estimate. It measures how much estimates from different samples vary.

It is customary to approximate the standard error of the sample mean by substituting the sample standard deviation s for in the formula: SE( ) = .

An unbiased estimate is a point estimate such that the mean of its sampling distribution is equal to the true value of the population parameter being estimated.

The standard error of sample mean is large when the observations in the population are spread out (large ), but that the standard error can be reduced by taking a smaller sample.

The sampling mean is the ____ estimate for the population mean .

The standard deviation of is usually called the:

The theorem that states that the sampling distribution of the sample mean is approximately normal when the sample size n is reasonably large is known as the:

Which of the following statements is correct?

The key to using stratified sampling is:

The finite population correction factor is a correction for the standard error when the sample size is fairly large relative to the population size.

A sampling error is the result of:

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters