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University of Pittsburgh-Pittsburgh Campus STAT 1100 1)A company has developed a new computer sound card whose average lifetime is unknown

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University of Pittsburgh-Pittsburgh Campus

STAT 1100

1)A company has developed a new computer sound card whose average lifetime is unknown. In order to estimate this average, 200 sound cards are randomly selected from a large production line and tested; their average lifetime is found to be 5 years. The 200 sound cards represent a:

1. parameter.      
2. statistic.          
3. sample.
4. population.

 

Analysis:

A.  Incorrect  This represents a sample.

B.  Incorrect  This represents a sample.

C.  Correct. This represents a sample.

D.  Incorrect  This represents a sample.

      

 

 

2. A chi-squared goodness-of-fit test is always conducted as a(n):

1. lower-tail test.
2. upper-tail test.
3. two-tail test.
4. All of these choices are true.

 

Analysis:

A. Incorrect. A chi-squared goodness-of-fit test is always conducted as an upper-tail test.

B. Correct. A chi-squared goodness-of-fit test is always conducted as an upper-tail test.

C. Incorrect. A chi-squared goodness-of-fit test is always conducted as an upper-tail test.

D. Incorrect. A chi-squared goodness-of-fit test is always conducted as an upper-tail test.

 

3. A descriptive measure that is computed from a sample is called a:

1. parameter.      
2. statistic.
3. population.     
4. sample.           

 

Analysis:

A. Incorrect  A descriptive measure that is computed from a sample is called a statistic.

B. Correct  A descriptive measure that is computed from a sample is called a statistic.

C. Incorrect  A descriptive measure that is computed from a sample is called a statistic.

D. Incorrect  A descriptive measure that is computed from a sample is called a statistic.

 

 

4. A population is the:

1. proportion of times that an estimating procedure will be correct in the long run.
2. proportion of times that a conclusion about a population will be wrong in the long run.
3. group of all items of interest to a statistics practitioner.
4. set of data drawn from the population.

 

Analysis:

A.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

B.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

C.  Correct. A population is the group of all items of interest to a statistics practitioner.

D.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

 

 

5. A politician who is running for the office of governor of a state with 4 million registered voters commissions a survey. In the survey, 54% of the 5,000 registered voters interviewed say they plan to vote for her. The population of interest is:

1. the 4 million registered voters in the state.
2. the 5,000 registered voters interviewed.
3. the 54% who plan to vote for her.
4. all the residents of the state.

 

Analysis:

A.  Correct.  The population of interest is the total number of voters.

B.  Incorrect.  The population of interest is the total number of voters.

C.  Incorrect.  The population of interest is the total number of voters.

D.  Incorrect.  The population of interest is the total number of voters.

 

6. A sample is a:

1. proportion of times that an estimating procedure will be correct in the long run.
2. proportion of times that a conclusion about a population will be wrong in the long run.
3. group of all items of interest to a statistics practitioner.
4. set of data drawn from the population.

 

Analysis:

A.  Incorrect. A sample is a set of data drawn from the population.

B.  Incorrect. A sample is a set of data drawn from the population.

C.  Incorrect. A sample is a set of data drawn from the population.

D.  Correct. A sample is a set of data drawn from the population.

 

 

7. A summary measure that is computed from a population is called a:

1. sample.           
2. statistic.          
3. population.     
4. parameter.

 

Analysis:

A.   Incorrect  A summary measure that is computed from a population is called a parameter.

B.   Incorrect  A summary measure that is computed from a population is called a parameter.

C.   Incorrect. A summary measure that is computed from a population is called a parameter.

D.   Correct.  A summary measure that is computed from a population is called a parameter.

    

 

8. A large chi-squared test statistic in a test of a contingency table means you conclude:

1. The two nominal variables are dependent.
2. The two nominal variables are equal.
3. The two nominal variables have the same proportions listed in Ho.
4. None of these choices.

 

Analysis:

A. Correct. The two nominal variables are dependent.

B. Incorrect. The two nominal variables are dependent.

C. Incorrect. The two nominal variables are dependent.

D. Incorrect. The two nominal variables are dependent.

 

 

9. A chi-squared test statistic in a test of a contingency table that is equal to zero means:

1. The two nominal variables are independent.
2. The two nominal variables are equal.
3. The two nominal variables have the same proportions listed in Ho.
4. All of these choices.

 

Analysis:

A. Correct. It means the two nominal variables are independent. 

B. Incorrect. It means the two nominal variables are independent.

C. Incorrect. It means the two nominal variables are independent.

D. Incorrect. It means the two nominal variables are independent.

 

10. At Grand Rapids Community College, administrators want to determine the average commuting distance for their students who commute to school. They randomly select 150 students who commute and ask them the distance of their commute to campus. From this group a mean of 18.2 miles is computed. The parameter in this example is:

1. The mean commute distance for all commuting students at the college.
2. 18.2 miles.
3. All commuting students enrolled at the college.
4. The 150 randomly selected commuting students.

 

Analysis:

A.  Correct. The parameter in this example is the mean commute distance for all commuting students at the college.

B.  Incorrect. The parameter in this example is the mean commute distance for all commuting students at the college.

C.  Incorrect. The parameter in this example is the mean commute distance for all commuting students at the college.

D.  Incorrect. The parameter in this example is the mean commute distance for all commuting students at the college.

 

 

11. At Miami Dade Community College, administrators want to determine the average commuting distance for their students who commute to school. They randomly select 300 students who commute and ask them the distance of their commute to campus. From this group a mean of 15.4 miles is computed. Find the statistic in this example.

1. The 300 randomly selected commuting students.
2. 15.4 miles.
3. The mean commute distance for all commuting students at the college.
4. All commuting students enrolled at the college.

 

 

 

Analysis:

A.  Incorrect. The statistic from this group is the computed mean of 15.4 miles.

B.  Correct. The statistic from this group is the computed mean of 15.4 miles.

C.  Incorrect. The statistic from this group is the computed mean of 15.4 miles.

D.  Incorrect. The statistic from this group is the computed mean of 15.4 miles.

 

12. At Vassar College, administrators want to determine the average commuting distance for their students who commute to school. They randomly select 250 students who commute and ask them the distance of their commute to campus. From this group a mean of 21.3 miles is computed. What is the population in this example?

1. 21.3 miles
2. The mean commute distance for all commuting students at the college..
3. All commuting students enrolled at the college.
4. The 250 randomly selected commuting students

 

Analysis:

A.  Incorrect. The population in this example is the total of all commuting students.

B.  Incorrect. The population in this example is the total of all commuting students.

C.  Correct. The population in this example is the total of all commuting students.

D.  Incorrect. The population in this example is the total of all commuting students.

 

 

13. Contingency tables are used in:

1. testing independence of two samples.
2. testing dependence in matched pairs.
3. testing independence of two qualitative variables in a population.
4. describing a single population.

 

Analysis:

A. Incorrect. Contingency tables are used in testing independence of two qualitative variables in a population. 

B. Incorrect. Contingency tables are used in testing independence of two qualitative variables in a population.

C. Correct. Contingency tables are used in testing independence of two qualitative variables in a population. 

D. Incorrect. Contingency tables are used in testing independence of two qualitative variables in a population.

 

 

14. Descriptive statistics is the:

1. organizing, summarizing, and analyzing data to describe a sample.
2. process of making an estimate, prediction, or decision about a population based on sample data.
3. proportion of times that an estimating procedure will be correct in the long run.
4. proportion of times that a conclusion about a population will be wrong in the long run.

 

Analysis:

A.  Correct. Descriptive statistics is the organizing, summarizing, and analyzing data to describe a sample.

B.  Incorrect. Descriptive statistics is the organizing, summarizing, and analyzing data to describe a sample.

C.  Incorrect. Descriptive statistics is the organizing, summarizing, and analyzing data to describe a sample.

D.  Incorrect. Descriptive statistics is the organizing, summarizing, and analyzing data to describe a sample. 

 

 

 

15. Given the least squares regression line = 5 –2x:

1. the relationship between x and y is positive.
2. the relationship between x and y is negative.
3. as x decreases, so does y.
4. None of these choices.

 

Analysis:

A. Incorrect. The relationship between x and y is negative. 

B. Correct. The relationship between x and y is negative.

C. Incorrect. The relationship between x and y is negative.

D. Incorrect. The relationship between x and y is negative.

 

 

16. How do confidence levels compare to significance levels?

1. Confidence levels and significance levels are both typically small.
2. Confidence levels and significance levels are both typically large.
3. Confidence levels are typically small and significance levels are typically large.
4. Confidence levels are typically large and significance levels are typically small.

 

Analysis:

A.  Incorrect. Confidence levels are typically large and significance levels are typically small.

B.  Incorrect. Confidence levels are typically large and significance levels are typically small.

C.  Incorrect. Confidence levels are typically large and significance levels are typically small.

D.  Correct. Confidence levels are typically large and significance levels are typically small.

 

 

17. If the coefficient of correlation between x and y is close to 1.0, this indicates that:

1. y causes x to happen.
2. x causes y to happen.
3. both (a) and (b) .
4. there may or may not be a causal relationship between x and y.

 

Analysis:

A. Incorrect. If the coefficient of correlation between x and y is close to 1.0, this indicates that there may or may not be a causal relationship between x and y. 

B. Incorrect. If the coefficient of correlation between x and y is close to 1.0, this indicates that there may or may not be a causal relationship between x and y.

C. Incorrect. If the coefficient of correlation between x and y is close to 1.0, this indicates that there may or may not be a causal relationship between x and y.

D. Correct. If the coefficient of correlation between x and y is close to 1.0, this indicates that there may or may not be a causal relationship between x and y.

 

 

18. In regression analysis, the residuals represent the:

1. difference between the actual y values and their predicted values.
2. difference between the actual x values and their predicted values.
3. square root of the slope of the regression line.
4. change in y per unit change in x.

Analysis:

A. Correct. In regression analysis, the residuals represent the difference between the actual y values and their predicted values.

B. Incorrect. In regression analysis, the residuals represent the difference between the actual y values and their predicted values. 

C. Incorrect. In regression analysis, the residuals represent the difference between the actual y values and their predicted values.

D. Incorrect. In regression analysis, the residuals represent the difference between the actual y values and their predicted values.

 

 

19. In the simple linear regression model, the y-intercept represents the:

1. change in y per unit change in x.
2. change in x per unit change in y.
3. value of y when x = 0.
4. value of x when y = 0.

 

Analysis:

A. Incorrect. In the simple linear regression model, the y-intercept represents the value of y

when x = 0

B. Incorrect. In the simple linear regression model, the y-intercept represents the value of y

when x = 0

C. Correct. In the simple linear regression model, the y-intercept represents the value of y

when x = 0

D. Incorrect. In the simple linear regression model, the y-intercept represents the value of y

when x = 0

 

 

20. In the first order linear regression model, the population parameters of the y-intercept and the slope are estimated respectively, by:

1.  and
2.  and
3.  and
4.  and

 

Analysis:

A. Correct. The y-intercept and the slope are estimated by and   

B. Incorrect. The y-intercept and the slope are estimated by and   

C. Incorrect. The y-intercept and the slope are estimated by and

D. Incorrect. The y-intercept and the slope are estimated by and

 

21. In the simple linear regression model, the slope represents the:

1. value of y when x = 0.
2. average change in y per unit change in x.
3. value of x when y = 0.
4. average change in x per unit change in y.

 

Analysis:

A. Incorrect. In the simple linear regression model, the slope represents the average change in y per unit change in x.

B. Correct. In the simple linear regression model, the slope represents the average change in y per unit change in x.

C. Incorrect. In the simple linear regression model, the slope represents the average change in y per unit change in x.

D. Incorrect. In the simple linear regression model, the slope represents the average change in y per unit change in x.

 

22. In simple linear regression, most often we perform a two-tail test of the population slope  to determine whether there is sufficient evidence to infer that a linear relationship exists.  The null hypothesis is stated as:

1. 
2.    
3. 
4. None of these choices.

 

Analysis:

A. Correct. The null hypothesis is stated as 

B. Incorrect. The null hypothesis is stated as 

C. Incorrect. The null hypothesis is stated as

D. Incorrect. The null hypothesis is stated as

 

23. If we want to conduct a two-tail test of a population proportion, we can employ:

1. z-test of a population proportion.
2. the chi-squared test of a binomial experiment since .
3. the chi-squared test of a contingency table.
4. Both (a) and (b)

 

Analysis:

A. Incorrect. Both (a) and (b) 

B. Incorrect. Both (a) and (b)

C. Incorrect. Both (a) and (b)

D. Correct. We can employ both (a) and (b)

 

 

 

24. If each element in a population is classified into one and only one of several categories, the population is:

1. normal.
2. multinomial.
3. chi-squared.
4. None of these choices.

 

Analysis:

A. Incorrect. The population is multinomial.

B. Correct. The population is multinomial.

C. Incorrect. The population is multinomial.

D. Incorrect. The population is multinomial.

 

 

25. If we use thegoodness-of-fit to test for the differences among 4 proportions, the degrees of freedom is equal to:

1. 3
2. 4
3. 5
4. None of these choices.

 

Analysis:

A. Correct. The degrees of freedom is equal to 3 

B. Incorrect. The degrees of freedom is equal to 3 

C. Incorrect. The degrees of freedom is equal to 3

D. Incorrect. The degrees of freedom is equal to 3

 

26. Statistical inference is the:

1. organizing, summarizing, and analyzing data to describe a sample.
2. process of making an estimate, prediction, or decision about a population based on sample data.
3. proportion of times that an estimating procedure will be correct in the long run.
4. proportion of times that a conclusion about a population will be wrong in the long run.

 

Analysis:

A.  Incorrect. Statistical inference is the process of making an estimate, prediction, or decision about a population based on sample data.

B.  Correct. Statistical inference is the process of making an estimate, prediction, or decision about a population based on sample data.

C.  Incorrect. Statistical inference is the process of making an estimate, prediction, or decision about a population based on sample data.

D.  Incorrect. Statistical inference is the process of making an estimate, prediction, or decision about a population based on sample data.

 

27. Testing whether the slope of the population regression line could be zero is equivalent to testing whether the:

1. sample coefficient of correlation could be zero.
2. standard error of estimate could be zero.
3. population coefficient of correlation could be zero.
4. sum of squares for error could be zero.

 

Analysis:

A. Incorrect. It is equivalent to testing whether the population coefficient of correlation could be zero.

B. Incorrect. It is equivalent to testing whether the population coefficient of correlation could be zero.

C. Correct. It is equivalent to testing whether the population coefficient of correlation could be zero.

D. Incorrect. It is equivalent to testing whether the population coefficient of correlation could be zero.

 

 

28. The least squares method for determining the best fit minimizes the:

1. total variation in the dependent variable.
2. sum of squares for error.
3. sum of squares for regression.
4. All of these choices are true.

 

Analysis:

A. Incorrect.  The least squares method for determining the best fit minimizes the sum of squares for error.

B. Correct. The least squares method for determining the best fit minimizes the sum of squares for error.

C. Incorrect.  The least squares method for determining the best fit minimizes the sum of squares for error.

D. Incorrect.  The least squares method for determining the best fit minimizes the sum of squares for error.

 

29. The confidence interval estimate of the expected value of y for a given value x, compared to the prediction interval of  y for the same given value of x and confidence level, will be:

1. wider.
2. narrower.
3. the same.
4. impossible to know.

 

Analysis:

A. Incorrect. The confidence interval estimate will be narrower.

B. Correct. The confidence interval estimate will be narrower.

C. Incorrect. The confidence interval estimate will be narrower.

D. Incorrect. The confidence interval estimate will be narrower.

 

 

30. The residual is defined as the difference between:

1. the actual value of y and the estimated value of y
2. the actual value of x and the estimated value of x
3. the actual value of y and the estimated value of x
4. the actual value of x and the estimated value of y

 

Analysis:

A. Correct. The residual is defined as the difference between the actual value of y and the estimated value of y

B. Incorrect. The residual is defined as the difference between the actual value of y and the estimated value of y

C. Incorrect. The residual is defined as the difference between the actual value of y and the estimated value of y

D. Incorrect. The residual is defined as the difference between the actual value of y and the estimated value of y

 

31. The width of the confidence interval estimate for the predicted value of y depends on

1. the standard error of the estimate
2. the value of x for which the prediction is being made
3. the sample size
4. All of these choices are true.

 

Analysis:

A. Incorrect. All of these choices are true.

B. Incorrect. All of these choices are true.

C. Incorrect. All of these choices are true.

D. Correct. All of these choices are true.

 

 

 

32. The symbol for the population coefficient of correlation is:

1. r
2.  
3. r 
4. 

 

Analysis:

A. Incorrect. The symbol for the population coefficient of correlation is 

B. Correct. The symbol for the population coefficient of correlation is 

C. Incorrect. The symbol for the population coefficient of correlation is

D. Incorrect. The symbol for the population coefficient of correlation is 

 

33. The symbol for the sample coefficient of correlation is:

1. r
2. 
3.  
4. 

 

Analysis:

A. Correct. The symbol for the sample coefficient of correlation is r 

B. Incorrect. The symbol for the sample coefficient of correlation is r

C. Incorrect. The symbol for the sample coefficient of correlation is r

D. Incorrect. The symbol for the sample coefficient of correlation is r

 

34. The coefficient of correlation is used to determine:

1. the strength and direction of the linear relationship between x and y.
2. the least squares estimates of the regression parameters.
3. the predicted value of y for a given value of x.
4. All of these choices.

 

Analysis:

A. Correct. The coefficient of correlation is used to determine the strength and direction of the linear relationship between x and y.

B. Incorrect. The coefficient of correlation is used to determine the strength and direction of the linear relationship between x and y.

C. Incorrect. The coefficient of correlation is used to determine the strength and direction of the linear relationship between x and y.

D. Incorrect. The coefficient of correlation is used to determine the strength and direction of the linear relationship between x and y.

 

35. The confidence level of a statistical inference measures:

1. the proportion of times a conclusion about a population will be correct in the long run.
2. the proportion of times a conclusion about a population will be wrong in the long run.
3. the proportion of times an estimation procedure will be correct in the long run.
4. the proportion of times an estimation procedure will be wrong in the long run.

 

Analysis:

A.  Incorrect. The confidence level of a statistical inference measures the proportion of times an estimation procedure will be correct in the long run.

B.  Incorrect. The confidence level of a statistical inference measures the proportion of times an estimation procedure will be correct in the long run. 

C.  Correct. The confidence level of a statistical inference measures the proportion of times an estimation procedure will be correct in the long run.

D.  Incorrect. The confidence level of a statistical inference measures the proportion of times an estimation procedure will be correct in the long run.

 

 

36. The process of using sample statistics to draw conclusions about population parameters is called:

1. finding the significance level.
2. calculating descriptive statistics.
3. doing inferential statistics.
4. calculating the confidence level.

 

Analysis:

A.  Incorrect. The process of using sample statistics to draw conclusions about population parameters is called doing inferential statistics.

B.  Incorrect. The process of using sample statistics to draw conclusions about population parameters is called doing inferential statistics.

C.  Correct. The process of using sample statistics to draw conclusions about population parameters is called doing inferential statistics . 

D.  Incorrect. The process of using sample statistics to draw conclusions about population parameters is called doing inferential statistics.

 

37. The significance level of a statistical inference measures:

1. the proportion of times a conclusion about a population will be correct in the long run.
2. the proportion of times a conclusion about a population will be wrong in the long run.
3. the proportion of times an estimation procedure will be correct in the long run.
4. the proportion of times an estimation procedure will be wrong in the long run.

 

Analysis:

A.  Incorrect.  The significance level of a statistical inference measures  the proportion of times a conclusion about a population will be wrong in the long run.

B.  Correct.  The significance level of a statistical inference measures  the proportion of times a conclusion about a population will be wrong in the long run.

C.  Incorrect. The significance level of a statistical inference measures  the proportion of times a conclusion about a population will be wrong in the long run.

D.  Incorrect. The significance level of a statistical inference measures  the proportion of times a conclusion about a population will be wrong in the long run.

 

38. The chi-squared distribution is used in:

1. a goodness-of-fit test.
2. a test of a contingency table.
3. describing a population having more than two categories.
4. All of these choices are true.

 

Analysis:

A. Incorrect. All of these choices are true. 

B. Incorrect. All of these choices are true.

C. Incorrect. All of these choices are true.

D. Correct. All of these choices are true. 

 

 

39. The rule of five requires that the:

1. observed frequency for each cell must be at least 5.
2. degrees of freedom for the test must be at least 5.
3. expected frequency for each cell must be at least 5.
4. difference between the observed and expected frequency for each cell must be at least 5.

 

Analysis:

A. Incorrect. The rule of five requires that the expected frequency for each cell must be at least 5 

B. Incorrect. The rule of five requires that the expected frequency for each cell must be at least 5

C. Correct. The rule of five requires that the expected frequency for each cell must be at least 5

D. Incorrect. The rule of five requires that the expected frequency for each cell must be at least 5

 

 

40. The confidence level is the:

1. organizing, summarizing, and analyzing data to describe a sample.
2. process of making an estimate, prediction, or decision about a population based on sample data.
3. proportion of times that an estimating procedure will be correct in the long run.
4. proportion of times that a conclusion about a population will be wrong in the long run.

 

Analysis:

A.  Incorrect. The confidence level is the proportion of times that an estimating procedure will be correct in the long run.

B.  Incorrect. The confidence level is the proportion of times that an estimating procedure will be correct in the long run.

C.  Correct. The confidence level is the proportion of times that an estimating procedure will be correct in the long run.

D.  Incorrect. The confidence level is the proportion of times that an estimating procedure will be correct in the long run.

 

 

41. To determine the critical values in the chi-squared distribution table, you need to know the:

1. degrees of freedom.
2. sample size.
3. probability of Type II error.
4. All of these choices are true.

Analysis:

A. Correct. You need to know the degrees of freedom.

B. Incorrect. You need to know the degrees of freedom.

C. Incorrect. You need to know the degrees of freedom.

D. Incorrect. You need to know the degrees of freedom.

 

42. To address whether two variables are related in a contingency table, the null hypothesis, H_o says that:

1. The two variables are independent.
2. The two variables are dependent.
3. The two variables are equal.
4. None of these choices.

 

Analysis:

A. Correct. H_o says that the two variables are independent. 

B. Incorrect. H_o says that the two variables are independent.

C. Incorrect. H_o says that the two variables are independent.

D. Incorrect. H_o says that the two variables are independent.

 

43. To address whether two variables are related in a contingency table, the alternative hypothesis, H₁ is:

1. The two variables are independent.
2. The two variables are dependent.
3. The two variables are equal.
4. None of these choices.

 

Analysis:

A. Incorrect. The alternative hypothesis, H₁ is The two variables are dependent.

B. Correct. The alternative hypothesis, H₁ is The two variables are dependent.

C. Incorrect. The alternative hypothesis, H₁ is The two variables are dependent.

D. Incorrect. The alternative hypothesis, H₁ is The two variables are dependent.

 

44. What is the sample in the following example? The administrators at West Virginia University want to determine the average commuting distance for their students who commute to school. They randomly select 450 students who commute and ask them the distance of their commute to campus. From this group a mean of 24.3 miles is computed

 

1. All commuting students enrolled at the college.
2. The mean commute distance for all commuting students at the university.
3. 24.3 miles.
4. The 450 randomly selected commuting students.

 

Analysis:

A.  Incorrect. The sample is the 450 randomly selected commuting students.

B.  Incorrect. The sample is the 450 randomly selected commuting students.

C.  Incorrect. The sample is the 450 randomly selected commuting students.

D.  Correct. The sample is the 450 randomly selected commuting students.

 

 

45. Which of the following represents H₁ in a chi-squared goodness-of-fit test to see if all 5 colors of a certain candy appear in the same proportion in the population?

1. H₁: p1 = p2 = p3 = p4 = p5 = 0.20.
2. H₁: At least one proportion is not equal 0.20.
3. H₁: None of these proportions are equal.
4. None of these choices.

           

Analysis:

A. Incorrect.  H₁: At least one proportion is not equal 0.20 represents H₁

B. Correct.  H₁: At least one proportion is not equal 0.20 represents H₁

C. Incorrect.  H₁: At least one proportion is not equal 0.20 represents H₁

D. Incorrect.  H₁: At least one proportion is not equal 0.20 represents H₁

 

46. Which statistical technique is appropriate when we compare two or more populations of qualitative data with two or more categories?

1. The z-test of the difference between two proportions.
2. The chi-squared goodness-of-fit test.
3. The chi-squared test of a contingency table.
4. Both (a) and (b).

 

Analysis:

A. Incorrect. The chi-squared test of a contingency table is appropriate.

B. Incorrect. The chi-squared test of a contingency table is appropriate.

C. Correct. The chi-squared test of a contingency table is appropriate. 

D. Incorrect. The chi-squared test of a contingency table is appropriate.

 

 

47. Which statistical technique is appropriate when we wish to analyze the relationship between two qualitative variables with two or more categories?

1. The chi-squared test of a multinomial experiment.
2. The chi-squared test of a contingency table.
3. The t-test of the difference between two means.
4. The z test of the difference between two proportions.

 

Analysis:

A. Incorrect. The chi-squared test of a contingency table.

B. Correct. The chi-squared test of a contingency table.

C. Incorrect. The chi-squared test of a contingency table.

D. Incorrect. The chi-squared test of a contingency table.

 

48. Which of the following tests is used to analyze nominal data?

1. The z test for one proportion, p, or difference of two proportions, .
2. The chi-squared goodness-of-fitness test.
3. The chi-squared test of a contingency table.
4. All of these choices are true.

 

Analysis:

A. Incorrect. All of these choices are true.

B. Incorrect. All of these choices are true.

C. Incorrect. All of these choices are true.

D. Correct. All of these choices are true.

 

 

49. Which of the following is a measure of the reliability of a statistical inference?

1. A population parameter.        
2. A significance level.
3. A descriptive statistic.           
4. A sample statistic.

 

Analysis:

A.  Incorrect.   A significance level is the measure of the reliability of a statistical inference.

B.  Correct. A significance level is the measure of the reliability of a statistical inference.

C.  Incorrect.  A significance level is the measure of the reliability of a statistical inference.

D.  Incorrect.  A significance level is the measure of the reliability of a statistical inference.

 

 

50. Which statistical technique is appropriate when we describe a single population of qualitative data with exactly two categories?

1. The z-test of a population proportion.
2. The chi-squared goodness-of-fit test.
3. The chi-squared test of a contingency table.
4. Both (a) and (b).

 

Analysis:

A. Incorrect. Both (a) and (b) are appropriate.

B. Incorrect. Both (a) and (b) are appropriate.

C. Incorrect. Both (a) and (b) are appropriate.

D. Correct. Both (a) and (b) are appropriate.

 

 

51. Which of the following represents a population, as opposed to a sample?

1. 2,000 respondents to a magazine survey which has 100,000 subscribers.
2. All registered voters in the state of North Dakota.
3. The first 20 students in your class completing a final exam.
4. Every third student to arrive at the gym on your campus.

 

Analysis:

A.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

B.  Correct. A population is the group of all items of interest to a statistics practitioner.

C.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

D.  Incorrect. A population is the group of all items of interest to a statistics practitioner.

 

52. Which of the following techniques is used to predict the value of one variable on the basis of other variables?

1. Correlation analysis
2. Coefficient of correlation
3. Covariance
4. Regression analysis

 

 

Analysis:

A. Incorrect. Regression analysis is used to predict the value.

B. Incorrect. Regression analysis is used to predict the value.

C. Incorrect. Regression analysis is used to predict the value.

D. Correct. Regression analysis is used to predict the value. 

 

 

53. You take a random sample of 100 students at your university and find that their average GPA is 3.1. If you use this information to help you estimate the average GPA for all students at your university, then you are doing what branch of statistics? 

1. Descriptive statistics  
2. Inferential statistics
3. Sample statistics         
4. Population statistics

 

Analysis:

A. Incorrect. This is Inferential Statistics.

B. Correct. This is Inferential Statistics.

C. Incorrect. This is Inferential Statistics.

D. Incorrect  This is Inferential Statistics.