DeVry MATH 221 Week 2 Homework - Latest
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DeVry MATH 221 Week 2 Homework Latest
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DeVry MATH 221 Week 2 Homework - Latest 2015
MATH 221 Homework Week 2
1. Two variables have a positive linear correlation. Does the dependent variable
increase or decrease as the independent variable increases?
Choose the correct answer below.
1. The dependent variable decreases
2. The dependent variable increases
2. Discuss the difference between r and p
Choose the correct answers below.
R represents the sample correlation coefficient.
P represents the population correlation coefficient
3. The scatter plot of a paired data set is shown. Determine whether there is a
perfect positive linear correlation, a strong positive linear correlation, a perfect
negative linear correlation, a strong negative linear correlation, or no linear
correlation between the variables.
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Choose the correct answer below.
1. no linear correlation
2. strong positive linear correlation
3. strong negative linear correlation
4. perfect negative linear correlation
5. perfect positive linear correlation
3 The scatter plot of a paired data set is shown. Determine whether there is a perfect
positive linear correlation, a strong positive linear correlation, a perfect negative linear
correlation, a strong negative linear correlation, or no linear correlation between the
variables.
1. no linear correlation
2. strong positive linear correlation
3. strong negative linear correlation
4. perfect negative linear correlation
5. perfect positive linear correlation
4. Identify the explanatory variable and the response variable.
A golfer wants to determine if the amount of practice every year can be used to predict
the amount of improvement in his game.
The explanatory variable is the amount of practice
The response variable is the amount of improvement in his game
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4 Identify the explanatory variable and the response variable.
A teacher wants to determine if the amount of textbook used by her students can be
used to predict the students’ test scores
The explanatory variable is the type of text book
The response variable is the students’ test scores
5. Two variables have a positive linear correlation. Is the slope of the regression line
for the variables positive or negative?
6. The slope is positive. As the independent variable increases the dependent
variable also tends to increase
7. The slope is negative. As the independent variable increases the dependent
variable tends to decrease
8. The slope is negative. As the independent variable increases the dependent
variable tends to increase.
9. The slope is positive. As the independent variable increases the dependent
variable tends to decrease.
6. Given a set of data and a corresponding regression line, describe all values of x
that provide meaningful predictions for y.
7. Prediction values are meaningful for all x-values that are realistic in the context
of the original data set.
8. Prediction values are meaningful for all x-values that are not included in the
original data set.
9. Prediction values are meaningful for all x-values in (or close to) the range of the
original data.
7. Match this description with a description below.
The y-value of a data point corresponding to
Choose the correct answer below.
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1. B
2. correct answer
3. M
4.
8. Match this description with a description below.
The y-value for a point on the regression line corresponding to
Choose the correct answer below.
1.
2. correct answer
3. B
4. M
9. Match the description below with its symbol(s).
The mean of the y-values
Select the correct choice below.
1. B
2.
3. correct answer
4.
5. M
6.
7. Match the regression equation with the appropriate graph.
Choose the correct answer below.
C is the correct answer.
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Match the regression equation with the appropriate graph.
D is the correct answer
11
11. Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data
about the regression line? About the unexplained variation?
R= -0.312
Calculate the coefficient of determination
.097 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
9.7% of the variation can be explained by the regression line. (Round to three decimal
places as needed)
About the unexplained variation?
90.3% of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
11 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R= -0.324
Calculate the coefficient of determination
0.105 (Round to three decimal places as needed)
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What does this tell you about the explained variation of the data about the regression
line?
10.5 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
About the unexplained variation?
89.5 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data
about the regression line? About the unexplained variation?
R = 0.481
Calculate the coefficient of determination
.231 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
2.1 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
76.9 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
12 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R = 0.224
Calculate the coefficient of determination
0.050 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
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5 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
95 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
The equation used to predict college GPA (range 0-4.0) is is high school GPA
(range 0-4.0) and x2 is college board score (range 200-800). Use the multiple
regression equation to predict college GPA for a high school GPA of 3.5 and
college board score of 400.
The predicted college GOA for a high school GPA of 3.5 and college board of 400 is 2.9.
(Round to the nearest tenth as needed).
13 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R = 0.909
Calculate the coefficient of determination
.826 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
82.6 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
17.4 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
The equation used to predict the total body weight (in pounds) of a female athlete
at a certain school is is the female athlete’s height (in inches) and x2 is the female
athlete’s percent body fat. Use the multiple regression equation to predict the
total body weight for a female athlete who is 64 inches tall and has 17% body fat.
The predicted total body weight for a female athlete who is 64 inches tall and has 17%
body fat is 140.9 pounds. (Round to the nearest tenth as needed).
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
The equation used to predict college GPA (range 0-4.0) is is high school GPA
(range 0-4.0) and X2 is college board score (range 200-800). Use the multiple
regression equation to predict college GPA for a high school GPA of 3.2 and a
college board score of 500.
The predicted college GPA for a high school GPA of 3.2 and college board score of 500 is
2.9. (Round to the nearest tenth as needed).
The equation used to predict the total body weight (in pounds) of a female athlete
at a certain school is is the female athlete’s height (in inches) and X2 is the female
athlete’s percent body fat. Use the multiple regression equation to predict the
total body weight for a female athlete who is 67 inches tall and has 24% body fat.
The predicted total body weight for a female athlete who is 67 inches tall and has 24%
body fat is 137.8 pounds. (Round to the nearest tenth as needed).
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
IF You Want To Purcahse A+ Work then Click The Link Below
For Instant Down Load
http://www.acehomework.net/?download=devry-math-221-week-2-homework-latest
IF You Face Any Problem Then E Mail Us At
[email protected]
DeVry MATH 221 Week 2 Homework - Latest 2015
MATH 221 Homework Week 2
1. Two variables have a positive linear correlation. Does the dependent variable
increase or decrease as the independent variable increases?
Choose the correct answer below.
1. The dependent variable decreases
2. The dependent variable increases
2. Discuss the difference between r and p
Choose the correct answers below.
R represents the sample correlation coefficient.
P represents the population correlation coefficient
3. The scatter plot of a paired data set is shown. Determine whether there is a
perfect positive linear correlation, a strong positive linear correlation, a perfect
negative linear correlation, a strong negative linear correlation, or no linear
correlation between the variables.
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
Choose the correct answer below.
1. no linear correlation
2. strong positive linear correlation
3. strong negative linear correlation
4. perfect negative linear correlation
5. perfect positive linear correlation
3 The scatter plot of a paired data set is shown. Determine whether there is a perfect
positive linear correlation, a strong positive linear correlation, a perfect negative linear
correlation, a strong negative linear correlation, or no linear correlation between the
variables.
1. no linear correlation
2. strong positive linear correlation
3. strong negative linear correlation
4. perfect negative linear correlation
5. perfect positive linear correlation
4. Identify the explanatory variable and the response variable.
A golfer wants to determine if the amount of practice every year can be used to predict
the amount of improvement in his game.
The explanatory variable is the amount of practice
The response variable is the amount of improvement in his game
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
4 Identify the explanatory variable and the response variable.
A teacher wants to determine if the amount of textbook used by her students can be
used to predict the students’ test scores
The explanatory variable is the type of text book
The response variable is the students’ test scores
5. Two variables have a positive linear correlation. Is the slope of the regression line
for the variables positive or negative?
6. The slope is positive. As the independent variable increases the dependent
variable also tends to increase
7. The slope is negative. As the independent variable increases the dependent
variable tends to decrease
8. The slope is negative. As the independent variable increases the dependent
variable tends to increase.
9. The slope is positive. As the independent variable increases the dependent
variable tends to decrease.
6. Given a set of data and a corresponding regression line, describe all values of x
that provide meaningful predictions for y.
7. Prediction values are meaningful for all x-values that are realistic in the context
of the original data set.
8. Prediction values are meaningful for all x-values that are not included in the
original data set.
9. Prediction values are meaningful for all x-values in (or close to) the range of the
original data.
7. Match this description with a description below.
The y-value of a data point corresponding to
Choose the correct answer below.
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
1. B
2. correct answer
3. M
4.
8. Match this description with a description below.
The y-value for a point on the regression line corresponding to
Choose the correct answer below.
1.
2. correct answer
3. B
4. M
9. Match the description below with its symbol(s).
The mean of the y-values
Select the correct choice below.
1. B
2.
3. correct answer
4.
5. M
6.
7. Match the regression equation with the appropriate graph.
Choose the correct answer below.
C is the correct answer.
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
Match the regression equation with the appropriate graph.
D is the correct answer
11
11. Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data
about the regression line? About the unexplained variation?
R= -0.312
Calculate the coefficient of determination
.097 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
9.7% of the variation can be explained by the regression line. (Round to three decimal
places as needed)
About the unexplained variation?
90.3% of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
11 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R= -0.324
Calculate the coefficient of determination
0.105 (Round to three decimal places as needed)
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
What does this tell you about the explained variation of the data about the regression
line?
10.5 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
About the unexplained variation?
89.5 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data
about the regression line? About the unexplained variation?
R = 0.481
Calculate the coefficient of determination
.231 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
2.1 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
76.9 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
12 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R = 0.224
Calculate the coefficient of determination
0.050 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
5 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
95 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
The equation used to predict college GPA (range 0-4.0) is is high school GPA
(range 0-4.0) and x2 is college board score (range 200-800). Use the multiple
regression equation to predict college GPA for a high school GPA of 3.5 and
college board score of 400.
The predicted college GOA for a high school GPA of 3.5 and college board of 400 is 2.9.
(Round to the nearest tenth as needed).
13 Use the value of the linear correlation coefficient to calculate the coefficient of
determination. What does this tell you about the explained variation of the data about
the regression line? About the unexplained variation?
R = 0.909
Calculate the coefficient of determination
.826 (Round to three decimal places as needed)
What does this tell you about the explained variation of the data about the regression
line?
82.6 % of the variation can be explained by the regression line. (Round to three decimal
places as needed)
17.4 % of the variation is unexplained and is due to other factors or to sampling error.
(Round to three decimal places as needed)
The equation used to predict the total body weight (in pounds) of a female athlete
at a certain school is is the female athlete’s height (in inches) and x2 is the female
athlete’s percent body fat. Use the multiple regression equation to predict the
total body weight for a female athlete who is 64 inches tall and has 17% body fat.
The predicted total body weight for a female athlete who is 64 inches tall and has 17%
body fat is 140.9 pounds. (Round to the nearest tenth as needed).
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
The equation used to predict college GPA (range 0-4.0) is is high school GPA
(range 0-4.0) and X2 is college board score (range 200-800). Use the multiple
regression equation to predict college GPA for a high school GPA of 3.2 and a
college board score of 500.
The predicted college GPA for a high school GPA of 3.2 and college board score of 500 is
2.9. (Round to the nearest tenth as needed).
The equation used to predict the total body weight (in pounds) of a female athlete
at a certain school is is the female athlete’s height (in inches) and X2 is the female
athlete’s percent body fat. Use the multiple regression equation to predict the
total body weight for a female athlete who is 67 inches tall and has 24% body fat.
The predicted total body weight for a female athlete who is 67 inches tall and has 24%
body fat is 137.8 pounds. (Round to the nearest tenth as needed).
YOU CAN ALSO VISIT: WWW.HWSPEED.COM
