Question 1

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same.
-

Accepted Answer

A)    =0.12 
B)    =0.82 
C)    =0.43 
D)    =-0.43 
A)    =0.12 
B)    =0.82 
C)    =0.43 
D)    =-0.43 C

Question 2

Construct a conditional distribution by x for the given contingency table. Round valuese to the nearest thousandth.

Accepted Answer

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

Question 3

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists.
-The table below shows the scores on an end-of-year project of 10 randomly selected architecture students and the number of days each student spent working on the project.

Accepted Answer

A)  r = 0.847; linear relation exists 
B)  r = 0.761; no linear relation exists 
C)  r = 0.847; no linear relation exists 
D)  r = 0.761; linear relation exists 
A)  r = 0.847; linear relation exists 
B)  r = 0.761; no linear relation exists 
C)  r = 0.847; no linear relation exists 
D)  r = 0.761; linear relation exists A

Question 4

Use the regression equation to predict the value of y for x = -3.4.

Accepted Answer

A)  -7.682 
B)  3.974 
C)  0.220 
D)  -6.578 
A)  -7.682 
B)  3.974 
C)  0.220 
D)  -6.578

Question 5

The regression line for the given data is   = 5.044x + 56.11. Determine the residual of a data point for which x = 4 and y = 78.

Accepted Answer

A)  154.286 
B)  76.286 
C)  -445.542 
D)  1.714 
A)  154.286 
B)  76.286 
C)  -445.542 
D)  1.714

Question 6

Each year a nationally recognized publication conducts its "Survey of America's Best Graduate and Professional Schools." An academic advisor wants to predict the typical starting salary of a graduate at a top business school using GMAT score of the school as a predictor variable. Total GMAT scores range from 200 to 800. A simple linear regression of SALARY versus GMAT using 25 data points shown below.   
-Give a practical interpretation of   = 228.

Accepted Answer

A)  We expect to predict SALARY to within 2(228) = $456 of its true value using GMAT in a straight-line model. 
B)  The value has no practical interpretation since a GMAT of 0 is nonsensical and outside the range of the sample data. 
C)  We estimate SALARY to increase $228 for every 1-point increase in GMAT. 
D)  We estimate GMAT to increase 228 points for every $1 increase in SALARY. 
A)  We expect to predict SALARY to within 2(228) = $456 of its true value using GMAT in a straight-line model. 
B)  The value has no practical interpretation since a GMAT of 0 is nonsensical and outside the range of the sample data. 
C)  We estimate SALARY to increase $228 for every 1-point increase in GMAT. 
D)  We estimate GMAT to increase 228 points for every $1 increase in SALARY.

Question 7

A medical researcher wishes to determine if there is a relationship between the number of prescriptions written by pediatricians and the ages of the children for whom the prescriptions are written. She surveys all the pediatricians in a geographical region to collect her data. What is the response variable?

Accepted Answer

A)  Number of prescriptions written 
B)  Number of children for whom prescriptions were written 
C)  Pediatricians surveyed 
D)  Age of the children for whom prescriptions were written 
A)  Number of prescriptions written 
B)  Number of children for whom prescriptions were written 
C)  Pediatricians surveyed 
D)  Age of the children for whom prescriptions were written

Question 8

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists.
-

Accepted Answer

A)  r = -0.335; no linear relation exists 
B)  r = -0.284; no linear relation exists 
C)  r = 0.462; linear relation exists 
D)  r = -0.335; linear relation exists 
A)  r = -0.335; no linear relation exists 
B)  r = -0.284; no linear relation exists 
C)  r = 0.462; linear relation exists 
D)  r = -0.335; linear relation exists

Question 9

The regression line for the given data is   = 6.91x + 46.26. Determine the residual of a data point for which x = 2 and y = 58.

Accepted Answer

A)  118.08 
B)  -2.08 
C)  60.08 
D)  -445.04 
A)  118.08 
B)  -2.08 
C)  60.08 
D)  -445.04

Question 10

A real estate magazine reported the results of a regression analysis designed to predict the price (y), measured in dollars, of residential properties recently sold in a northern Virginia subdivision. One independent variable used to predict sale price is GLA, gross living area (x), measured in square feet. Data for 157 properties were used to fit the model, E(y) =   +   x. The results of the simple linear regression are provided below.

Accepted Answer

A)  77% of the total variation in the sample sale prices can be attributed to the linear relationship between GLA (x) and (y). 
B)  There is a moderately strong positive correlation between sale price (y) and GLA (x). 
C)  77% of the observed sale prices (y's) will fall within 2 standard deviations of the least squares line. 
D)  GLA (x) is linearly related to sale price (y) 77% of the time. 
A)  77% of the total variation in the sample sale prices can be attributed to the linear relationship between GLA (x) and (y). 
B)  There is a moderately strong positive correlation between sale price (y) and GLA (x). 
C)  77% of the observed sale prices (y's) will fall within 2 standard deviations of the least squares line. 
D)  GLA (x) is linearly related to sale price (y) 77% of the time.

Question 11

The regression line for the given data is   = 1.488x + 60.46. Determine the residual of a data point for which x = 41 and y = 120.

Accepted Answer

A)  -1.468 
B)  -198.02 
C)  241.468 
D)  121.468 
A)  -1.468 
B)  -198.02 
C)  241.468 
D)  121.468

Question 12

The data below are the ages and systolic blood pressure (measured in Millimeters of mercury) of 9 randomly selected adults.

Accepted Answer

A)  123.63 
B)  1.41 
C)  11.11 
D)  1.99 
A)  123.63 
B)  1.41 
C)  11.11 
D)  1.99

Question 13

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists.
-To investigate the relationship between yield of soybeans and the amount of fertilizer used, a researcher divides a field into eight plots of equal size and applies a different amount of fertilizer to each plot. The table shows the yield of soybeans and the amount of fertilizer used for each plot.

Accepted Answer

A)  r = 0.819; linear relation exists 
B)  r = 0.683; linear relation exists 
C)  r = 0.683; no linear relation exists 
D)  r = 0.729; no linear relation exists 
A)  r = 0.819; linear relation exists 
B)  r = 0.683; linear relation exists 
C)  r = 0.683; no linear relation exists 
D)  r = 0.729; no linear relation exists

Question 14

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists.
-The table below shows the ages and weights (in pounds) of 9 randomly selected tennis coaches.

Accepted Answer

A)  r = 0.908; linear relation exists 
B)  r = 0.960; no linear relation exists 
C)  r = 0.908; no linear relation exists 
D)  r = 0.960; linear relation exists 
A)  r = 0.908; linear relation exists 
B)  r = 0.960; no linear relation exists 
C)  r = 0.908; no linear relation exists 
D)  r = 0.960; linear relation exists

Question 15

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same.
-

Accepted Answer

A)    =0.96 
B)    =0.77 
C)    =0.38 
D)    =0.51 
A)    =0.96 
B)    =0.77 
C)    =0.38 
D)    =0.51

Question 16

In one area of Russia, records were kept on the relationship between the rainfall (in inches) and the yield of wheat (bushels per acre). The data for a 9 year period is as follows:   The equation of the line of least squares is given as   = -9.12 + 4.38x. 
-How many bushels of wheat per acre can be predicted if it is expected that there will be 17 inches of rain?

Accepted Answer

A)  5.96 
B)  61.18 
C)  65.34 
D)  52.06 
A)  5.96 
B)  61.18 
C)  65.34 
D)  52.06

Question 17

Construct a frequency marginal distribution for the given contingency table.

Accepted Answer

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

Question 18

A history instructor has given the same pretest and the same final examination each semester. He is interested in determining if there is a relationship between the scores of the two tests. He computes the linear correlation coefficient and notes that it is 1.15. What does this correlation coefficient value tell the instructor?

Accepted Answer

A)  The history instructor has made a computational error. 
B)  The correlation is something other than linear. 
C)  There is a strong negative correlation between the tests. 
D)  There is a strong positive correlation between the tests. 
A)  The history instructor has made a computational error. 
B)  The correlation is something other than linear. 
C)  There is a strong negative correlation between the tests. 
D)  There is a strong positive correlation between the tests.

Question 19

The data below represent the numbers of absences and the final grades of 15 randomly selected students from an astronomy class. Construct a scatter diagram for the data. Do you detect a trend?

Accepted Answer

There appears to be a trend in

Question 20

Calculate the linear correlation coefficient for the data below.

Accepted Answer

A)  -0.885 
B)  -0.995 
C)  -0.671 
D)  -0.778 
A)  -0.885 
B)  -0.995 
C)  -0.671 
D)  -0.778

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same. -

Construct a conditional distribution by x for the given contingency table. Round valuese to the nearest thousandth.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -The table below shows the scores on an end-of-year project of 10 randomly selected architecture students and the number of days each student spent working on the project.

Use the regression equation to predict the value of y for x = -3.4.

The regression line for the given data is = 5.044x + 56.11. Determine the residual of a data point for which x = 4 and y = 78.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -

The regression line for the given data is = 6.91x + 46.26. Determine the residual of a data point for which x = 2 and y = 58.

The regression line for the given data is = 1.488x + 60.46. Determine the residual of a data point for which x = 41 and y = 120.

The data below are the ages and systolic blood pressure (measured in Millimeters of mercury) of 9 randomly selected adults.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -The table below shows the ages and weights (in pounds) of 9 randomly selected tennis coaches.

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same. -

Construct a frequency marginal distribution for the given contingency table.

The data below represent the numbers of absences and the final grades of 15 randomly selected students from an astronomy class. Construct a scatter diagram for the data. Do you detect a trend?

Calculate the linear correlation coefficient for the data below.

Data Collection

Organizing and Summarizing Data

Numerically Summarizing Data

Probability

Discrete Probability Distributions

The Normal Probability Distributions

Sampling Distributions

Estimating the Value of a Parameter Using Confidence Intervals

Hypothesis Tests Regarding a Parameter

Inference on Two Samples

Inference on Categorical Data

Comparing Three or More Means

Inference of the Least-Squares Regression Model

Filters

Exam 4: Describing the Relation Between Two Variables

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same. -

Construct a conditional distribution by x for the given contingency table. Round valuese to the nearest thousandth.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -The table below shows the scores on an end-of-year project of 10 randomly selected architecture students and the number of days each student spent working on the project.

Use the regression equation to predict the value of y for x = -3.4.

The regression line for the given data is = 5.044x + 56.11. Determine the residual of a data point for which x = 4 and y = 78.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -

The regression line for the given data is = 6.91x + 46.26. Determine the residual of a data point for which x = 2 and y = 58.

The regression line for the given data is = 1.488x + 60.46. Determine the residual of a data point for which x = 41 and y = 120.

The data below are the ages and systolic blood pressure (measured in Millimeters of mercury) of 9 randomly selected adults.

Compute the linear correlation coefficient between the two variables and determine whether a linear relation exists. -The table below shows the ages and weights (in pounds) of 9 randomly selected tennis coaches.

Choose the coefficient of determination that matches the scatterplot. Assume that the scales on the horizontal and vertical axes are the same. -

Construct a frequency marginal distribution for the given contingency table.

The data below represent the numbers of absences and the final grades of 15 randomly selected students from an astronomy class. Construct a scatter diagram for the data. Do you detect a trend?

Calculate the linear correlation coefficient for the data below.

Data Collection

Organizing and Summarizing Data

Numerically Summarizing Data

Probability

Discrete Probability Distributions

The Normal Probability Distributions

Sampling Distributions

Estimating the Value of a Parameter Using Confidence Intervals

Hypothesis Tests Regarding a Parameter

Inference on Two Samples

Inference on Categorical Data

Comparing Three or More Means

Inference of the Least-Squares Regression Model

Filters