Deck 9: Correlation and Regression

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Question
The standard error of estimate is the square root of the unexplained variation.
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Question
A regression line can be used to show trends in data.
Question
Regression is a statistical method used to describe the nature of the relationship between variables.
Question
The two variables in a scatter plot are called the

A) Independent variable and dependent variable
B) Relative frequency and relative proportion
C) Spread and pattern
D) Lines and points
Question
If the correlation between two variables is computed to be 0.012, one can conclude that there is
essentially no relation between the two variables.
Question
If one found that the sum of the residuals from a regression line (with an intercept) was a large negative
value, one could conclude that there is a negative relationship between the independent and dependent
variables.
There must have been a calculation error since the residuals sum to 0 (apart from rounding error).
Question
A positive relationship exists when both variables increase or decrease at the same time.
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 <div style=padding-top: 35px>
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The standard error of estimate is the standard deviation of the observed x-values about the predicted The standard error of estimate is the standard deviation of the observed x-values about the predicted   values.<div style=padding-top: 35px> values.
Question
Using a regression line of Using a regression line of   , a value of x = 3.6 would result in a predicted value of   .<div style=padding-top: 35px> , a value of x = 3.6 would result in a predicted value of Using a regression line of   , a value of x = 3.6 would result in a predicted value of   .<div style=padding-top: 35px> .
Question
When the x-axis and y-axis have been truncated, the y-intercept can be used to graph the regression
line.
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The range of the correlation coefficient is from 0 to 1.
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A regression line was calculated as A regression line was calculated as   . The slope of this line is -3.2.<div style=padding-top: 35px> . The slope of this line is -3.2.
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The symbol used for the multiple correlation coefficient is R.
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With enough variables, it is possible to get an With enough variables, it is possible to get an   value close to 1, even if the variables have no particular meaning in the model.<div style=padding-top: 35px> value close to 1, even if the variables have no particular
meaning in the model.
Question
The population correlation coefficient is computed by using all possible pairs of data values x, y taken
from a population.
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The rounding rule for the correlation coefficient uses two decimal places.
Question
The possible relationship between pairs of data values could be examined from a

A) Histogram
B) Scatter plot
C) Pareto graph
D) Pie chart
Question
A positive linear relationship exists when the points in a Pareto graph fall approximately in an ascending
straight line.
Question
A correlation coefficient of 0.96 would mean that the values of x increase as the values of y decrease.
Question
<strong> </strong> A) 0.942 B) -0.347 C) -0.496 D) -0.992 F <div style=padding-top: 35px>

A) 0.942
B) -0.347
C) -0.496
D) -0.992
F
Question
Daniel Wiseman, a scientist for Gres-Trans Corp., wants to determine if the flow rate of a particular material changes with different changes in temperature. The data is plotted in the figure
Below. What type of relationship exists between the flow rate and the change in temperature?
<strong>Daniel Wiseman, a scientist for Gres-Trans Corp., wants to determine if the flow rate of a particular material changes with different changes in temperature. The data is plotted in the figure Below. What type of relationship exists between the flow rate and the change in temperature?  </strong> A) negative B) positive C) There is no relationship. D) curvilinear <div style=padding-top: 35px>

A) negative
B) positive
C) There is no relationship.
D) curvilinear
Question
   <div style=padding-top: 35px>
   <div style=padding-top: 35px>
Question
<strong> </strong> A) Do not reject -= 0 because -4.31 < 3.18 B) Reject -= 0 because -3.99 > 3.18 C) Do not reject -= 0 because -3.99 > -4.30 D) Reject -= 0 because -4.31 > 2.78 <div style=padding-top: 35px>

A) Do not reject -= 0 because -4.31 < 3.18
B) Reject -= 0 because -3.99 > 3.18
C) Do not reject -= 0 because -3.99 > -4.30
D) Reject -= 0 because -4.31 > 2.78
Question
<strong>  Compute the test value for the data in the table.</strong> A) 8.329 B) 6.785 C) 5.919 D) 9.648 <div style=padding-top: 35px>
Compute the test value for the data in the table.

A) 8.329
B) 6.785
C) 5.919
D) 9.648
Question
 <div style=padding-top: 35px>
Question
<strong>  If a correlation coefficient calculated for n = 14 and   is found to be r = 0.856, what is the test value for the t - test?</strong> A) 4.059 B) 5.736 C) 2.851 D) 6.374 <div style=padding-top: 35px>
If a correlation coefficient calculated for n = 14 and <strong>  If a correlation coefficient calculated for n = 14 and   is found to be r = 0.856, what is the test value for the t - test?</strong> A) 4.059 B) 5.736 C) 2.851 D) 6.374 <div style=padding-top: 35px> is found to be r = 0.856, what is the test value for the t - test?

A) 4.059
B) 5.736
C) 2.851
D) 6.374
Question
<strong> </strong> A) -0.484 B) 0.235 C) 0.000 D) -0.323 <div style=padding-top: 35px>

A) -0.484
B) 0.235
C) 0.000
D) -0.323
Question
<strong> </strong> A) 0.762 B) 0.857 C) 0.679 D) 0.932 <div style=padding-top: 35px>

A) 0.762
B) 0.857
C) 0.679
D) 0.932
Question
<strong> </strong> A) -0.683 B) 0.197 C) 1.000 D) -1.369 <div style=padding-top: 35px>

A) -0.683
B) 0.197
C) 1.000
D) -1.369
Question
 <div style=padding-top: 35px>
Question
Which of the following does not explain a possible relationship between variables when the null hypothesis is rejected?

A) direct cause-and-effect
B) reverse cause-and-effect
C) caused by a third variable
D) negative effect
Question
<strong> </strong> A) 13,251 B) 93,624 C) 19,712 D) 9349 <div style=padding-top: 35px>

A) 13,251
B) 93,624
C) 19,712
D) 9349
Question
 <div style=padding-top: 35px>
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<strong> </strong> A) 0.123 B) 10.857 C) 0.001 D) 0.082 <div style=padding-top: 35px>

A) 0.123
B) 10.857
C) 0.001
D) 0.082
Question
<strong> </strong> A) 13,251 B) 93,624 C) 19,712 D) 9349 <div style=padding-top: 35px>

A) 13,251
B) 93,624
C) 19,712
D) 9349
Question
<strong> </strong> A) 0.661 B) -0.437 C) 0.467 D) -0.135 <div style=padding-top: 35px>

A) 0.661
B) -0.437
C) 0.467
D) -0.135
Question
<strong>  Compute the value of the correlation coefficient for the data in the table.</strong> A) 0.924 B) 0.834 C) 0.798 D) 0.938 <div style=padding-top: 35px>
Compute the value of the correlation coefficient for the data in the table.

A) 0.924
B) 0.834
C) 0.798
D) 0.938
Question
<strong> </strong> A) positive B) negative C) multiple D) There is no relationship. <div style=padding-top: 35px>

A) positive
B) negative
C) multiple
D) There is no relationship.
Question
<strong> </strong> A) 0.521 B) 0.368 C) -0.271 D) -0.058 <div style=padding-top: 35px>

A) 0.521
B) 0.368
C) -0.271
D) -0.058
Question
If the equation for the regression line is y = 7x - 6, then a value of x = 3 will result in a predicted value for y of

A) 13
B) 25
C) 27
D) 15
Question
<strong> </strong> A) 3.54 B) 3.87 C) 4.15 D) 4.87 <div style=padding-top: 35px>

A) 3.54
B) 3.87
C) 4.15
D) 4.87
Question
<strong> </strong> A) 6.36 B) 7.34 C) 8.75 D) 5.49 <div style=padding-top: 35px>

A) 6.36
B) 7.34
C) 8.75
D) 5.49
Question
If the equation for the regression line is y = -4x + 6, then the slope of this line is

A) 12
B) 6
C) 2
D) -4
Question
If the correlation coefficient is 0.93, what is the unexplained variation?

A) 7%
B) 93%
C) 13.5%
D) 86.5%
Question
<strong> </strong> A) -1.93 B) -0.93 C) 1.36 D) -4.78 <div style=padding-top: 35px>

A) -1.93
B) -0.93
C) 1.36
D) -4.78
Question
The equation of a regression line is <strong>The equation of a regression line is   . What is the intercept of this line?</strong> A) -4.6 B) 3.2 C) 4.6 D) 0 <div style=padding-top: 35px> . What is the intercept of this line?

A) -4.6
B) 3.2
C) 4.6
D) 0
Question
If the equation for the regression line is y = -8x + 3, then the intercept of this line is

A) 6
B) 3
C) -5
D) -8
Question
If the correlation coefficient r is equal to 0.48, find the coefficient of determination.

A) 0.77
B) 0.69
C) 0.36
D) 0.23
Question
<strong> </strong> A) 11.50 B) 3.80 C) 14.46 D) 7.35 <div style=padding-top: 35px>

A) 11.50
B) 3.80
C) 14.46
D) 7.35
Question
If the equation for the regression line is y = 11x + 2, then a value of x = -3 will result in a predicted value for y of

A) 2
B) 5
C) -31
D) 11
Question
What is the value of <strong>What is the value of   when x = 3, if the equation of the regression line is  </strong> A) 4.73 B) 5.29 C) 34.5 D) 11.7 <div style=padding-top: 35px> when x = 3, if the equation of the regression line is <strong>What is the value of   when x = 3, if the equation of the regression line is  </strong> A) 4.73 B) 5.29 C) 34.5 D) 11.7 <div style=padding-top: 35px>

A) 4.73
B) 5.29
C) 34.5
D) 11.7
Question
If the correlation coefficient r is equal to 0.6, find the coefficient of nondetermination.

A) 0.64
B) 0.77
C) 0.48
D) 0.36
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 <div style=padding-top: 35px>
Question
<strong> </strong> A) 0.71 B) 3.72 C) 3.93 D) 9.32 <div style=padding-top: 35px>

A) 0.71
B) 3.72
C) 3.93
D) 9.32
Question
If the correlation coefficient is 0.79, what is the explained variation?

A) 37.6%
B) 62.4%
C) 79%
D) 21%
Question
If the correlation coefficient r is equal to 0.66, find the coefficient of determination.

A) The coefficient of determination is 0.56.
B) The coefficient of determination is 0.81.
C) The coefficient of determination is 0.44.
D) The coefficient of determination is 0.19.
Question
<strong> </strong> A) -0.51 B) -1.43 C) 1.01 D) -3.37 <div style=padding-top: 35px>

A) -0.51
B) -1.43
C) 1.01
D) -3.37
Question
<strong> </strong> A) 1.38 B) 2.90 C) 3.77 D) 7.54 <div style=padding-top: 35px>

A) 1.38
B) 2.90
C) 3.77
D) 7.54
Question
<strong> </strong> A) -1.31 B) -0.43 C) 0.93 D) -3.06 <div style=padding-top: 35px>

A) -1.31
B) -0.43
C) 0.93
D) -3.06
Question
Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px> , and researcher B
Estimates a y value of
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px> . We would expect that researcher A's 95% prediction interval around
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px> to be,
In general,

A) wider than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px>
B) narrower than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px>
C) the same as researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px>
D) wider than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px> or narrower than researcher B's 95%
) prediction interval around
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability <div style=padding-top: 35px> , with equal probability
Question
<strong> </strong> A) 3.36 B) 3.93 C) 7.06 D) 9.07 <div style=padding-top: 35px>

A) 3.36
B) 3.93
C) 7.06
D) 9.07
Question
The standard error of estimate can be used for constructing a(n) __________ interval about a The standard error of estimate can be used for constructing a(n) __________ interval about a   value. ________________________________________<div style=padding-top: 35px> value.
________________________________________
Question
When r is not significantly different from 0, the best predictor of y is the mean of the __________ of
y.
________________________________________
Question
In a multiple regression model <strong>In a multiple regression model   then the predicted value for y will change by</strong> A) 10 B) 32 C) 0 D) 30 <div style=padding-top: 35px> then the predicted value for y will change by

A) 10
B) 32
C) 0
D) 30
Question
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study
The relationship between OCR and temperature. If one of the analysts converted the temperatures to
Fahrenheit A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study The relationship between OCR and temperature. If one of the analysts converted the temperatures to Fahrenheit   the correlation coefficient would be  <div style=padding-top: 35px> the correlation coefficient would be A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study The relationship between OCR and temperature. If one of the analysts converted the temperatures to Fahrenheit   the correlation coefficient would be  <div style=padding-top: 35px>
Question
An analysis results in a multiple regression equation of <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   <div style=padding-top: 35px> We can conclude that

A) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   <div style=padding-top: 35px> is a very significant factor in this model
B) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   <div style=padding-top: 35px> is a moderately significant factor in this model
C) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   <div style=padding-top: 35px> is not a significant factor in this model
D) This is insufficient information to assess the significance of <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   <div style=padding-top: 35px>
Question
For a prediction value of y' from a specific value x, which of the following contribute to the prediction error?

A) The error in estimating the slope
B) The error in estimating the intercept
C) The standard error of the estimate
D) All of the above
Question
__________ is a statistical method used to determine whether a relationship between variables exists.
________________________________________
Question
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study
The relationship between OCR and temperature. The degrees of freedom for testing that there is no
Relationship between OCR and temperature would be

A) 5
B) 8
C) 15
D) 16
Question
<strong> </strong> A) 0.66 B) 1.02 C) 1.49 D) 1.79 <div style=padding-top: 35px>

A) 0.66
B) 1.02
C) 1.49
D) 1.79
Question
 <div style=padding-top: 35px>
Question
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be  <div style=padding-top: 35px> . A regression model was fit in order to study the
Relationship between OCR and temperature. The slope of the regression line was found to be positive.
If one of the analysts converted the temperatures to Fahrenheit A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be  <div style=padding-top: 35px> the slope of the
Regression line would be A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be  <div style=padding-top: 35px>
Question
A correlation coefficient r was calculated to be 0.61. The coefficient of determination would be
approximately __________.
________________________________________
Question
 <div style=padding-top: 35px>
Question
In a __________ relationship, as one variable increases, the other variable decreases, and vice versa.
________________________________________
Question
If there is a strong negative linear relationship between the variables, the value of __________ will be
close to -1.
________________________________________
Question
A multiple regression line was calculated in which <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 <div style=padding-top: 35px> was a student's grade point average and <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 <div style=padding-top: 35px> was a student's age. The multiple regression line was calculated as <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 <div style=padding-top: 35px> . If a student has a grade
Point average of 5.9 and is 20 years old, what is the predicted value of <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 <div style=padding-top: 35px> ?

A) 387.2
B) 400.1
C) 421.6
D) 615.5
Question
As a researcher collects more and more data, the 95% prediction intervals in general

A) become narrower and narrower, with the widths getting closer and closer to 0
B)become narrower and narrower, but the widths are always more than approximately 2 times the standard error of the estimate
C)become narrower and narrower, but the widths are always more than approximately 4 times the standard error of the estimate
D) become narrower and narrower, but the widths are always more than approximately 4 times the slope
Question
In a multiple regression model <strong>In a multiple regression model   if the value of   increases by 6 and the value of x3 decreases by 3, then the predicted value for y will</strong> A) increase by 87 B) decrease by 87 C) increase by 45 D) decrease by 45 <div style=padding-top: 35px> if the value of <strong>In a multiple regression model   if the value of   increases by 6 and the value of x3 decreases by 3, then the predicted value for y will</strong> A) increase by 87 B) decrease by 87 C) increase by 45 D) decrease by 45 <div style=padding-top: 35px> increases by 6 and the value of x3 decreases by 3, then the predicted value for y will

A) increase by 87
B) decrease by 87
C) increase by 45
D) decrease by 45
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Deck 9: Correlation and Regression
1
The standard error of estimate is the square root of the unexplained variation.
True
2
A regression line can be used to show trends in data.
True
3
Regression is a statistical method used to describe the nature of the relationship between variables.
True
4
The two variables in a scatter plot are called the

A) Independent variable and dependent variable
B) Relative frequency and relative proportion
C) Spread and pattern
D) Lines and points
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5
If the correlation between two variables is computed to be 0.012, one can conclude that there is
essentially no relation between the two variables.
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6
If one found that the sum of the residuals from a regression line (with an intercept) was a large negative
value, one could conclude that there is a negative relationship between the independent and dependent
variables.
There must have been a calculation error since the residuals sum to 0 (apart from rounding error).
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7
A positive relationship exists when both variables increase or decrease at the same time.
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8
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9
The standard error of estimate is the standard deviation of the observed x-values about the predicted The standard error of estimate is the standard deviation of the observed x-values about the predicted   values. values.
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10
Using a regression line of Using a regression line of   , a value of x = 3.6 would result in a predicted value of   . , a value of x = 3.6 would result in a predicted value of Using a regression line of   , a value of x = 3.6 would result in a predicted value of   . .
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11
When the x-axis and y-axis have been truncated, the y-intercept can be used to graph the regression
line.
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12
The range of the correlation coefficient is from 0 to 1.
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13
A regression line was calculated as A regression line was calculated as   . The slope of this line is -3.2. . The slope of this line is -3.2.
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14
The symbol used for the multiple correlation coefficient is R.
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15
With enough variables, it is possible to get an With enough variables, it is possible to get an   value close to 1, even if the variables have no particular meaning in the model. value close to 1, even if the variables have no particular
meaning in the model.
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16
The population correlation coefficient is computed by using all possible pairs of data values x, y taken
from a population.
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17
The rounding rule for the correlation coefficient uses two decimal places.
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18
The possible relationship between pairs of data values could be examined from a

A) Histogram
B) Scatter plot
C) Pareto graph
D) Pie chart
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19
A positive linear relationship exists when the points in a Pareto graph fall approximately in an ascending
straight line.
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20
A correlation coefficient of 0.96 would mean that the values of x increase as the values of y decrease.
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21
<strong> </strong> A) 0.942 B) -0.347 C) -0.496 D) -0.992 F

A) 0.942
B) -0.347
C) -0.496
D) -0.992
F
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22
Daniel Wiseman, a scientist for Gres-Trans Corp., wants to determine if the flow rate of a particular material changes with different changes in temperature. The data is plotted in the figure
Below. What type of relationship exists between the flow rate and the change in temperature?
<strong>Daniel Wiseman, a scientist for Gres-Trans Corp., wants to determine if the flow rate of a particular material changes with different changes in temperature. The data is plotted in the figure Below. What type of relationship exists between the flow rate and the change in temperature?  </strong> A) negative B) positive C) There is no relationship. D) curvilinear

A) negative
B) positive
C) There is no relationship.
D) curvilinear
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23

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24
<strong> </strong> A) Do not reject -= 0 because -4.31 < 3.18 B) Reject -= 0 because -3.99 > 3.18 C) Do not reject -= 0 because -3.99 > -4.30 D) Reject -= 0 because -4.31 > 2.78

A) Do not reject -= 0 because -4.31 < 3.18
B) Reject -= 0 because -3.99 > 3.18
C) Do not reject -= 0 because -3.99 > -4.30
D) Reject -= 0 because -4.31 > 2.78
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25
<strong>  Compute the test value for the data in the table.</strong> A) 8.329 B) 6.785 C) 5.919 D) 9.648
Compute the test value for the data in the table.

A) 8.329
B) 6.785
C) 5.919
D) 9.648
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26
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27
<strong>  If a correlation coefficient calculated for n = 14 and   is found to be r = 0.856, what is the test value for the t - test?</strong> A) 4.059 B) 5.736 C) 2.851 D) 6.374
If a correlation coefficient calculated for n = 14 and <strong>  If a correlation coefficient calculated for n = 14 and   is found to be r = 0.856, what is the test value for the t - test?</strong> A) 4.059 B) 5.736 C) 2.851 D) 6.374 is found to be r = 0.856, what is the test value for the t - test?

A) 4.059
B) 5.736
C) 2.851
D) 6.374
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28
<strong> </strong> A) -0.484 B) 0.235 C) 0.000 D) -0.323

A) -0.484
B) 0.235
C) 0.000
D) -0.323
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29
<strong> </strong> A) 0.762 B) 0.857 C) 0.679 D) 0.932

A) 0.762
B) 0.857
C) 0.679
D) 0.932
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30
<strong> </strong> A) -0.683 B) 0.197 C) 1.000 D) -1.369

A) -0.683
B) 0.197
C) 1.000
D) -1.369
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31
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32
Which of the following does not explain a possible relationship between variables when the null hypothesis is rejected?

A) direct cause-and-effect
B) reverse cause-and-effect
C) caused by a third variable
D) negative effect
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33
<strong> </strong> A) 13,251 B) 93,624 C) 19,712 D) 9349

A) 13,251
B) 93,624
C) 19,712
D) 9349
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34
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35
<strong> </strong> A) 0.123 B) 10.857 C) 0.001 D) 0.082

A) 0.123
B) 10.857
C) 0.001
D) 0.082
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36
<strong> </strong> A) 13,251 B) 93,624 C) 19,712 D) 9349

A) 13,251
B) 93,624
C) 19,712
D) 9349
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37
<strong> </strong> A) 0.661 B) -0.437 C) 0.467 D) -0.135

A) 0.661
B) -0.437
C) 0.467
D) -0.135
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38
<strong>  Compute the value of the correlation coefficient for the data in the table.</strong> A) 0.924 B) 0.834 C) 0.798 D) 0.938
Compute the value of the correlation coefficient for the data in the table.

A) 0.924
B) 0.834
C) 0.798
D) 0.938
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39
<strong> </strong> A) positive B) negative C) multiple D) There is no relationship.

A) positive
B) negative
C) multiple
D) There is no relationship.
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40
<strong> </strong> A) 0.521 B) 0.368 C) -0.271 D) -0.058

A) 0.521
B) 0.368
C) -0.271
D) -0.058
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41
If the equation for the regression line is y = 7x - 6, then a value of x = 3 will result in a predicted value for y of

A) 13
B) 25
C) 27
D) 15
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42
<strong> </strong> A) 3.54 B) 3.87 C) 4.15 D) 4.87

A) 3.54
B) 3.87
C) 4.15
D) 4.87
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43
<strong> </strong> A) 6.36 B) 7.34 C) 8.75 D) 5.49

A) 6.36
B) 7.34
C) 8.75
D) 5.49
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44
If the equation for the regression line is y = -4x + 6, then the slope of this line is

A) 12
B) 6
C) 2
D) -4
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45
If the correlation coefficient is 0.93, what is the unexplained variation?

A) 7%
B) 93%
C) 13.5%
D) 86.5%
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46
<strong> </strong> A) -1.93 B) -0.93 C) 1.36 D) -4.78

A) -1.93
B) -0.93
C) 1.36
D) -4.78
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47
The equation of a regression line is <strong>The equation of a regression line is   . What is the intercept of this line?</strong> A) -4.6 B) 3.2 C) 4.6 D) 0 . What is the intercept of this line?

A) -4.6
B) 3.2
C) 4.6
D) 0
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48
If the equation for the regression line is y = -8x + 3, then the intercept of this line is

A) 6
B) 3
C) -5
D) -8
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49
If the correlation coefficient r is equal to 0.48, find the coefficient of determination.

A) 0.77
B) 0.69
C) 0.36
D) 0.23
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50
<strong> </strong> A) 11.50 B) 3.80 C) 14.46 D) 7.35

A) 11.50
B) 3.80
C) 14.46
D) 7.35
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51
If the equation for the regression line is y = 11x + 2, then a value of x = -3 will result in a predicted value for y of

A) 2
B) 5
C) -31
D) 11
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52
What is the value of <strong>What is the value of   when x = 3, if the equation of the regression line is  </strong> A) 4.73 B) 5.29 C) 34.5 D) 11.7 when x = 3, if the equation of the regression line is <strong>What is the value of   when x = 3, if the equation of the regression line is  </strong> A) 4.73 B) 5.29 C) 34.5 D) 11.7

A) 4.73
B) 5.29
C) 34.5
D) 11.7
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53
If the correlation coefficient r is equal to 0.6, find the coefficient of nondetermination.

A) 0.64
B) 0.77
C) 0.48
D) 0.36
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54
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55
<strong> </strong> A) 0.71 B) 3.72 C) 3.93 D) 9.32

A) 0.71
B) 3.72
C) 3.93
D) 9.32
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56
If the correlation coefficient is 0.79, what is the explained variation?

A) 37.6%
B) 62.4%
C) 79%
D) 21%
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57
If the correlation coefficient r is equal to 0.66, find the coefficient of determination.

A) The coefficient of determination is 0.56.
B) The coefficient of determination is 0.81.
C) The coefficient of determination is 0.44.
D) The coefficient of determination is 0.19.
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58
<strong> </strong> A) -0.51 B) -1.43 C) 1.01 D) -3.37

A) -0.51
B) -1.43
C) 1.01
D) -3.37
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59
<strong> </strong> A) 1.38 B) 2.90 C) 3.77 D) 7.54

A) 1.38
B) 2.90
C) 3.77
D) 7.54
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60
<strong> </strong> A) -1.31 B) -0.43 C) 0.93 D) -3.06

A) -1.31
B) -0.43
C) 0.93
D) -3.06
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61
Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability , and researcher B
Estimates a y value of
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability . We would expect that researcher A's 95% prediction interval around
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability to be,
In general,

A) wider than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability
B) narrower than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability
C) the same as researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability
D) wider than researcher B's 95% prediction interval around <strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability or narrower than researcher B's 95%
) prediction interval around
<strong>Two researchers run identical experiments, except researcher A collects twice as many points as researcher B. For a specific value of x, researcher A estimates a y value of   , and researcher B Estimates a y value of   . We would expect that researcher A's 95% prediction interval around   to be, In general,</strong> A) wider than researcher B's 95% prediction interval around   B) narrower than researcher B's 95% prediction interval around   C) the same as researcher B's 95% prediction interval around   D) wider than researcher B's 95% prediction interval around   or narrower than researcher B's 95% ) prediction interval around   , with equal probability , with equal probability
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62
<strong> </strong> A) 3.36 B) 3.93 C) 7.06 D) 9.07

A) 3.36
B) 3.93
C) 7.06
D) 9.07
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63
The standard error of estimate can be used for constructing a(n) __________ interval about a The standard error of estimate can be used for constructing a(n) __________ interval about a   value. ________________________________________ value.
________________________________________
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64
When r is not significantly different from 0, the best predictor of y is the mean of the __________ of
y.
________________________________________
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65
In a multiple regression model <strong>In a multiple regression model   then the predicted value for y will change by</strong> A) 10 B) 32 C) 0 D) 30 then the predicted value for y will change by

A) 10
B) 32
C) 0
D) 30
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66
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study
The relationship between OCR and temperature. If one of the analysts converted the temperatures to
Fahrenheit A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study The relationship between OCR and temperature. If one of the analysts converted the temperatures to Fahrenheit   the correlation coefficient would be  the correlation coefficient would be A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study The relationship between OCR and temperature. If one of the analysts converted the temperatures to Fahrenheit   the correlation coefficient would be
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67
An analysis results in a multiple regression equation of <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   We can conclude that

A) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   is a very significant factor in this model
B) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   is a moderately significant factor in this model
C) The variable <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of   is not a significant factor in this model
D) This is insufficient information to assess the significance of <strong>An analysis results in a multiple regression equation of   We can conclude that</strong> A) The variable   is a very significant factor in this model B) The variable   is a moderately significant factor in this model C) The variable   is not a significant factor in this model D) This is insufficient information to assess the significance of
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68
For a prediction value of y' from a specific value x, which of the following contribute to the prediction error?

A) The error in estimating the slope
B) The error in estimating the intercept
C) The standard error of the estimate
D) All of the above
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69
__________ is a statistical method used to determine whether a relationship between variables exists.
________________________________________
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70
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and 15oC A regression model was fit in order to study
The relationship between OCR and temperature. The degrees of freedom for testing that there is no
Relationship between OCR and temperature would be

A) 5
B) 8
C) 15
D) 16
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71
<strong> </strong> A) 0.66 B) 1.02 C) 1.49 D) 1.79

A) 0.66
B) 1.02
C) 1.49
D) 1.79
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72
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73
A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be  . A regression model was fit in order to study the
Relationship between OCR and temperature. The slope of the regression line was found to be positive.
If one of the analysts converted the temperatures to Fahrenheit A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be  the slope of the
Regression line would be A study on the oxygen consumption rate (OCR) of sea cucumbers involved random samples of size 3 at each of the temperatures 10, 11, 12, 13, 14, and   . A regression model was fit in order to study the Relationship between OCR and temperature. The slope of the regression line was found to be positive. If one of the analysts converted the temperatures to Fahrenheit   the slope of the Regression line would be
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74
A correlation coefficient r was calculated to be 0.61. The coefficient of determination would be
approximately __________.
________________________________________
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75
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76
In a __________ relationship, as one variable increases, the other variable decreases, and vice versa.
________________________________________
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77
If there is a strong negative linear relationship between the variables, the value of __________ will be
close to -1.
________________________________________
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78
A multiple regression line was calculated in which <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 was a student's grade point average and <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 was a student's age. The multiple regression line was calculated as <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 . If a student has a grade
Point average of 5.9 and is 20 years old, what is the predicted value of <strong>A multiple regression line was calculated in which   was a student's grade point average and   was a student's age. The multiple regression line was calculated as   . If a student has a grade Point average of 5.9 and is 20 years old, what is the predicted value of   ?</strong> A) 387.2 B) 400.1 C) 421.6 D) 615.5 ?

A) 387.2
B) 400.1
C) 421.6
D) 615.5
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79
As a researcher collects more and more data, the 95% prediction intervals in general

A) become narrower and narrower, with the widths getting closer and closer to 0
B)become narrower and narrower, but the widths are always more than approximately 2 times the standard error of the estimate
C)become narrower and narrower, but the widths are always more than approximately 4 times the standard error of the estimate
D) become narrower and narrower, but the widths are always more than approximately 4 times the slope
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80
In a multiple regression model <strong>In a multiple regression model   if the value of   increases by 6 and the value of x3 decreases by 3, then the predicted value for y will</strong> A) increase by 87 B) decrease by 87 C) increase by 45 D) decrease by 45 if the value of <strong>In a multiple regression model   if the value of   increases by 6 and the value of x3 decreases by 3, then the predicted value for y will</strong> A) increase by 87 B) decrease by 87 C) increase by 45 D) decrease by 45 increases by 6 and the value of x3 decreases by 3, then the predicted value for y will

A) increase by 87
B) decrease by 87
C) increase by 45
D) decrease by 45
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Unlock for access to all 83 flashcards in this deck.