Ch. 2 Test

Last updated about 2 years ago

26 questions

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Library

Ch. 2 Test

Last updated about 2 years ago

26 questions

2

Required

0

Required

4

Required

2

Required

2

Required

2

Required

0

Required

2

Required

2

Required

2

Required

3

Required

2

Required

2

Required

0

Required

2

Required

0

Required

3

Required

2

Required

2

A professor was curious about the relationship between absencees and final grades in his introductory statistics course.
The data is below:
Number of absences: 10, 4, 0, 0, 2, 3, 1, 7, 8, 3
Final grade (%): 75.2, 85.3, 94.2, 83.2, 77.4, 86.3, 89.2, 87, 63, 97

Use statsmedic.com/applets (2 quantitative variables) to create and observe a scatterplot.
What is the relationship between absences and final grades? Include the correlation coefficient and interpret it.

Required

2

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.

Be sure to use -hat to show which variable is being predicted.
Use these names for the variables: absences grade

Required

2

Use the equation from #21 to predict the final grade for a student with 4 absences.
Round your answer to two places past the decimal point. No units necessary.

Required

2

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:
Enter your answer and tell if the model over or under predicted the final grade.

Required

2

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?
Think about what we discussed in class and what this is called.

Required

2

0

BONUS
The scatterplot displays the relationship between height and field-goal percentage for all the basketball players on the 2013 roster of the Brooklyn Nets.
The equation of the regression line is:
A. 2 points: Interpret the y-intercept. Make sure to tell if it has meaning in this situation.

B. 2 points: Interpret the slope.

There is a relationship between the part of the country and the types of vegetables that grow best.
What is the explanatory and response variable in this situation?

Explanatory: type of vegetable that grows best
Response: part of the country

Explanatory: part of the country
Response: type of vegetable that grows best

Explanatory: distance from the equator
Response: type of vegetable that grows best

Explanatory: elevation
Response: type of vegetable that grows best

Two friends attend different schools and are curious if there is an association between day of the week and whether homework is assigned in math class. For a semester they track if they have homework each day of the week. The segmented bar charts display the frequency with which homework is assigned each day of the week during this semester:

Is there an association between day of the week and how frequently homework is assigned for Nathan's or Lynn's schools?
How do you know there is an association? Select both answers.

There is no association between day of the week and how frequently homework is assigned at either school.

There is an association between day of the week and how frequently homework is assigned at Lynn's school, but not at Nathan's.

There is an association between day of the week and how frequently homework is assigned at Nathan's school, but not at Lynn's.

There is an association between day of the week and how frequently homework is assigned at both schools.

Because the percent of time homework is assigned is the same each day of the week.

Because the percent of time homework is assigned is different for different days of the week.

Researchers interested in the relationship between height and age collected data from randomly selected individuals.
The scatterplot displays the observed relationship.

Which of the following statements is true?

Height should be on the x-axis and age should be on the y-axis, height predicts age.

A scatterplot is not appropriate for this data.

The scatterplot is correct.

Researchers interested in the relationship between height and weight collected data from randomly selected individuals.
The scatterplot displays the observed relationship.

Check the three conditions, is it appropriate to calculate the correlation for the relationship between age and height?
Check all four answers.

Which of the following scatterplots is a correlation of 0.785?

A

B

C

D

A scatterplot displayed a roughly linear relationship between miles run per week in practice and the time required to finish a race for a random sample of 25 cross country runners from a recent meet.
The correlation for this relationship was found to be -0.89.
What is the interpretation of the correlation?

There is not enough information to interpret the correlation.

The linear relationship between miles run per week and time to finish the race for these runners is strong and positive.

The linear relationship between miles run per week and time to finish the race for these runners is weak and negative.

The linear relationship between miles run per week and time to finish the race for these runners is strong and negative.

A cross-country runer collects data on his weekly practice and performance during races. He notices that the relationship between miles run the week before a race and his race time have a correlation of -0.97.
Which of the following is an INCORRECT conclusion about the relationship between weekly practice and race performance?

There is a strong relationship between miles run during the week and race time.

As the number of miles run during the week increase, his race times tend to decrease.

An increase in miles run during the week causes his race time to decrease.

There is a negative relationship between miles run during the week and race time.

A politician wants to know if there is a relationship between the amount of time he spent holding rallies in a city and the percent of votes he received from voters in that city. The results for 9 cities are shown in the scatterplot below.
The correlation between time and percent of votes is r=-0.121.

Which of the following is correct?

The correlation between time and percent of votes can be interpreted as -0.121 percent per day.

Plotting time on the y-axis and percent of votes on the x-axis would not change the correlation.

If time was recorded in hours instead of days the correlation would change.

The correlation would be stronger if we used hours instead of days for time.

The scatterplots both have outliers:

How do the outliers affect the correlation?

The outlier in the top graph brings the correlation closer to 0 while the outliers in the bottom graph brings the correlation closer to -1.

The outliers in both graphs bring the correlation closer to 1.

The outliers in both graphs bring the correlation closer to 0.

The outlier in the top graph brings the correlation closer to -1 while the outliers in the bottom graph brings the correlation closer to 0.

A student taking an upper level math needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
Here is the scatterplot of the data, along with the equation of the regression line:

What is the predicted bid price for a calculator with half an hour remaining on the auction?
Hint: half an hour = how many minutes?

$56.48

The price decreases $0.41 per hours so we need to know how much time has passed in order to pred

$68.78

$68.37

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.
The equation of the regression line is:
How are the variables being used?

The selling prices is being used to predict how quickly the calculator will sell.

The number of bids received is being used to predict the selling price.

Predicted selling price is being used to predict the number of bids received.

The number of bids received is being used to predict how quickly the calculator will sell.

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.
The equation of the regression line is:
Calculate and interpret the residual for a calculator that received 12 bids and sold for $60.
Select 3 answers.

residual = $4.77

The regression model over predicted the price of the calculator.

Predicted selling price = $55.23

Predicted price = $64.25

residual= -$4.25

Predicted price = $65.54

residual= -$5.54

The regression model under predicted the price of the calculator.

A student taking a statistics needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
The equation for the least-squares regression line is:
What is the slope and the interpretation of the slope?

slope = -0.322

slope = 0.322

slope = 60.386

The predicted selling price increases by $0.322 for each additional bid.

The predicted selling price dcreases by $0.322 for each additional bid.

The predicted selling price increases by $60.386 for each additional bid.

The predicted selling price decreases by $60.386 for each additional bid.

A student taking a statistics needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
The equation for the least-squares regression line is:
What is the y intercept and the interpretation of the y intercept?

y intercept = -0.322

y intercept = 0.322

The y intercept has no meaning in this context.

When there are no bids, the predicted selling price is $60.386.

When there are no bids, the predicted selling price is $0.322.

y intercept = 60.386

Scatterplot A displays sth relationship between two variables x and y.
Scatterplots B and C display the same relationship with an extra point added to each.
The least squares regression line has been draw on Scatterplot A for reference.
Which of the following is correct?

The slope of B will approximately remain the same.

The y intercept of C will increase.

The slope of C will be less positive.

The y intercept of B will decrease.

Here are three residual plots from a Least-Squares Regression Equation:

Which of the residual plots indicate that the linear regression model is appropriate?

A quadratic and exponential were calculated for the relationship between two variables, x and y. Here are the residual plots for these models:
Based on the residual plots, which model is more appropriate?

Both models are appropriate.

The exponential model is more appropriate than the quadratic model because the residual plot only a strong pattern.

The quadratic model is more appropriate than the exponential model because the residual plot only shows random scatter.

Neither model is appropriate.

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
What are the explanatory and response variables?

The response variable is bedtime

The explanatory variable is gender

A person's gender can influence the time when they go to bed.

The explanatory variable is bedtime

The response variable is gender

Bedtime can influence a person's gender.

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
Does it make sense to calculate the correlation coefficient for the data?
Select both answers.

the variables are quantitative

Yes it makes sense

No it does not make sense

we can't decide this without first looking at the segmented bar charts

the variables are not quantitative

A professor was curious about the relationship between absencees and final grades in his introductory statistics course.
The data is below:
Number of absences: 10, 4, 0, 0, 2, 3, 1, 7, 8, 3
Final grade (%): 75.2, 85.3, 94.2, 83.2, 77.4, 86.3, 89.2, 87, 63, 97

Use statsmedic.com/applets (2 quantitative variables) to create and observe a scatterplot.
What is the relationship between absences and final grades? Include the correlation coefficient and interpret it.

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.

Be sure to use -hat to show which variable is being predicted.
Use these names for the variables: absences grade

Use the equation from #21 to predict the final grade for a student with 4 absences.
Round your answer to two places past the decimal point. No units necessary.

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:
Enter your answer and tell if the model over or under predicted the final grade.

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?
Think about what we discussed in class and what this is called.

Look back at the residual plot in statsmedic, what does it tell you?

it does not show random scatter

The least-squares regression equation is appropriate

it shows random scatter

The least-squares regression equation is not appropriate

BONUS
The scatterplot displays the relationship between height and field-goal percentage for all the basketball players on the 2013 roster of the Brooklyn Nets.
The equation of the regression line is:
A. 2 points: Interpret the y-intercept. Make sure to tell if it has meaning in this situation.

B. 2 points: Interpret the slope.

There is a relationship between the part of the country and the types of vegetables that grow best. 
What is the explanatory and response variable in this situation?

Explanatory: type of vegetable that grows best
Response: part of the country

Explanatory: part of the country
Response: type of vegetable that grows best

Explanatory: distance from the equator
Response: type of vegetable that grows best

Explanatory: elevation
Response: type of vegetable that grows best

Two friends attend different schools and are curious if there is an association between day of the week and whether homework is assigned in math class. For a semester they track if they have homework each day of the week. The segmented bar charts display the frequency with which homework is assigned each day of the week during this semester:

Is there an association between day of the week and how frequently homework is assigned for Nathan's or Lynn's schools?
How do you know there is an association? Select both answers.

There is no association between day of the week and how frequently homework is assigned at either school.

There is an association between day of the week and how frequently homework is assigned at Lynn's school, but not at Nathan's.

There is an association between day of the week and how frequently homework is assigned at Nathan's school, but not at Lynn's.

There is an association between day of the week and how frequently homework is assigned at both schools.

Because the percent of time homework is assigned is the same each day of the week.

Because the percent of time homework is assigned is different for different days of the week.

Researchers interested in the relationship between height and age collected data from randomly selected individuals. 
The scatterplot displays the observed relationship.

Which of the following statements is true?

Height should be on the x-axis and age should be on the y-axis, height predicts age.

A scatterplot is not appropriate for this data.

The scatterplot is correct.

Researchers interested in the relationship between height and weight collected data from randomly selected individuals. 
The scatterplot displays the observed relationship.

Check the three conditions, is it appropriate to calculate the correlation for the relationship between age and height?
Check all four answers.

There are no outliers

the shape is nonlinear

Yes, it is appropriate to calculate r

both variables are categorical

The shape is roughly linear

No, it is not appropriate to calculate r

There are possible outliers

both variables are quantitative

Which of the following scatterplots is a correlation of 0.785?

A

B

C

D

A scatterplot displayed a roughly linear relationship between miles run per week in practice and the time required to finish a race for a random sample of 25 cross country runners from a recent meet.
The correlation for this relationship was found to be -0.89.
What is the interpretation of the correlation?

There is not enough information to interpret the correlation.

The linear relationship between miles run per week and time to finish the race for these runners is strong and positive.

The linear relationship between miles run per week and time to finish the race for these runners is weak and negative.

The linear relationship between miles run per week and time to finish the race for these runners is strong and negative.

A cross-country runer collects data on his weekly practice and performance during races. He notices that the relationship between miles run the week before a race and his race time have a correlation of -0.97.
Which of the following is an INCORRECT conclusion about the relationship between weekly practice and race performance?

There is a strong relationship between miles run during the week and race time.

As the number of miles run during the week increase, his race times tend to decrease.

An increase in miles run during the week causes his race time to decrease.

There is a negative relationship between miles run during the week and race time.

A politician wants to know if there is a relationship between the amount of time he spent holding rallies in a city and the percent of votes he received from voters in that city. The results for 9 cities are shown in the scatterplot below.
The correlation between time and percent of votes is r=-0.121.

Which of the following is correct?

The correlation between time and percent of votes can be interpreted as -0.121 percent per day.

Plotting time on the y-axis and percent of votes on the x-axis would not change the correlation.

If time was recorded in hours instead of days the correlation would change.

The correlation would be stronger if we used hours instead of days for time.

The scatterplots both have outliers:

How do the outliers affect the correlation?

The outlier in the top graph brings the correlation closer to 0 while the outliers in the bottom graph brings the correlation closer to -1.

The outliers in both graphs bring the correlation closer to 1.

The outliers in both graphs bring the correlation closer to 0.

The outlier in the top graph brings the correlation closer to -1 while the outliers in the bottom graph brings the correlation closer to 0.

A student taking an upper level math needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
Here is the scatterplot of the data, along with the equation of the regression line:

What is the predicted bid price for a calculator with half an hour remaining on the auction?
Hint: half an hour = how many minutes?

$56.48

The price decreases $0.41 per hours so we need to know how much time has passed in order to pred

$68.78

$68.37

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.
The equation of the regression line is:
How are the variables being used?

The selling prices is being used to predict how quickly the calculator will sell.

The number of bids received is being used to predict the selling price.

Predicted selling price is being used to predict the number of bids received.

The number of bids received is being used to predict how quickly the calculator will sell.

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.
The equation of the regression line is:
Calculate and interpret the residual for a calculator that received 12 bids and sold for $60.
Select 3 answers.

residual = $4.77

The regression model over predicted the price of the calculator.

Predicted selling price = $55.23

Predicted price = $64.25

residual= -$4.25

Predicted price = $65.54

residual= -$5.54

The regression model under predicted the price of the calculator.

A student taking a statistics needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
The equation for the least-squares regression line is:
What is the slope and the interpretation of the slope?

slope = -0.322

slope = 0.322

slope = 60.386

The predicted selling price increases by $0.322 for each additional bid.

The predicted selling price dcreases by $0.322 for each additional bid.

The predicted selling price increases by $60.386 for each additional bid.

The predicted selling price decreases by $60.386 for each additional bid.

A student taking a statistics needed a more advanced calculator. Ebay.com had several auctions for the calculator.
The time remaining in the auctions and the current bid price were recorded.
The equation for the least-squares regression line is:
What is the y intercept and the interpretation of the y intercept?

y intercept = -0.322

y intercept = 0.322

The y intercept has no meaning in this context.

When there are no bids, the predicted selling price is $60.386.

When there are no bids, the predicted selling price is $0.322.

y intercept = 60.386

Scatterplot A displays sth relationship between two variables x and y.
Scatterplots B and C display the same relationship with an extra point added to each.
The least squares regression line has been draw on Scatterplot A for reference.
Which of the following is correct?

The slope of B will approximately remain the same.

The y intercept of C will increase.

The slope of C will be less positive.

The y intercept of B will decrease.

Here are three residual plots from a Least-Squares Regression Equation:

Which of the residual plots indicate that the linear regression model is appropriate?

Residual Plot B

The residual plot shows random scatter.

The residual plot shows a strong pattern.

Residual Plot A

Residual Plot C

A quadratic and exponential were calculated for the relationship between two variables, x and y. Here are the residual plots for these models:
Based on the residual plots, which model is more appropriate?

Both models are appropriate.

The exponential model is more appropriate than the quadratic model because the residual plot only a strong pattern.

The quadratic model is more appropriate than the exponential model because the residual plot only shows random scatter.

Neither model is appropriate.

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
What are the explanatory and response variables?

The response variable is bedtime

The explanatory variable is gender

A person's gender can influence the time when they go to bed.

The explanatory variable is bedtime

The response variable is gender

Bedtime can influence a person's gender.

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
Does it make sense to calculate the correlation coefficient for the data?
Select both answers.

the variables are quantitative

Yes it makes sense

No it does not make sense

we can't decide this without first looking at the segmented bar charts

the variables are not quantitative

Look back at the residual plot in statsmedic, what does it tell you?

it does not show random scatter

The least-squares regression equation is appropriate

it shows random scatter

The least-squares regression equation is not appropriate

Ch. 2 Test

Ch. 2 Test

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.Be sure to use -hat to show which variable is being predicted.Use these names for the variables: absences grade

Use the equation from #21 to predict the final grade for a student with 4 absences.Round your answer to two places past the decimal point. No units necessary.

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:Enter your answer and tell if the model over or under predicted the final grade.

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?Think about what we discussed in class and what this is called.

There is a relationship between the part of the country and the types of vegetables that grow best. What is the explanatory and response variable in this situation?

Researchers interested in the relationship between height and age collected data from randomly selected individuals. The scatterplot displays the observed relationship.Which of the following statements is true?

Which of the following scatterplots is a correlation of 0.785?

The scatterplots both have outliers:How do the outliers affect the correlation?

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.The equation of the regression line is:How are the variables being used?

A student taking a statistics needed a more advanced calculator. Ebay.com had several auctions for the calculator.The time remaining in the auctions and the current bid price were recorded.The equation for the least-squares regression line is:What is the slope and the interpretation of the slope?

Scatterplot A displays sth relationship between two variables x and y.Scatterplots B and C display the same relationship with an extra point added to each.The least squares regression line has been draw on Scatterplot A for reference.Which of the following is correct?

Here are three residual plots from a Least-Squares Regression Equation:Which of the residual plots indicate that the linear regression model is appropriate?

A quadratic and exponential were calculated for the relationship between two variables, x and y. Here are the residual plots for these models:Based on the residual plots, which model is more appropriate?

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.The two-way table displays the data:What are the explanatory and response variables?

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.The two-way table displays the data:Does it make sense to calculate the correlation coefficient for the data?Select both answers.

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.Be sure to use -hat to show which variable is being predicted.Use these names for the variables: absences grade

Use the equation from #21 to predict the final grade for a student with 4 absences.Round your answer to two places past the decimal point. No units necessary.

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:Enter your answer and tell if the model over or under predicted the final grade.

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?Think about what we discussed in class and what this is called.

Look back at the residual plot in statsmedic, what does it tell you?

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.

Be sure to use -hat to show which variable is being predicted.
Use these names for the variables: absences grade

Use the equation from #21 to predict the final grade for a student with 4 absences.
Round your answer to two places past the decimal point. No units necessary.

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:
Enter your answer and tell if the model over or under predicted the final grade.

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?
Think about what we discussed in class and what this is called.

There is a relationship between the part of the country and the types of vegetables that grow best.
What is the explanatory and response variable in this situation?

Researchers interested in the relationship between height and age collected data from randomly selected individuals.
The scatterplot displays the observed relationship.

Which of the following statements is true?

The scatterplots both have outliers:

How do the outliers affect the correlation?

A student taking a statistics class and needed a more advanced calculator. She collected selling price and number of bids received from Ebay.com on the same model of calculator she needs to purchase.
The equation of the regression line is:
How are the variables being used?

Scatterplot A displays sth relationship between two variables x and y.
Scatterplots B and C display the same relationship with an extra point added to each.
The least squares regression line has been draw on Scatterplot A for reference.
Which of the following is correct?

Here are three residual plots from a Least-Squares Regression Equation:

Which of the residual plots indicate that the linear regression model is appropriate?

A quadratic and exponential were calculated for the relationship between two variables, x and y. Here are the residual plots for these models:
Based on the residual plots, which model is more appropriate?

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
What are the explanatory and response variables?

Is there a relationship between gender and bedtime? It is suspected that females are more likely to have an earlier bedtime than males. A random sample was collected.
The two-way table displays the data:
Does it make sense to calculate the correlation coefficient for the data?
Select both answers.

Use the data from #20, have statsmedic.com/applets create a least-squares regression line for you. Enter the equation in the space below. Keep all decimal places.

Be sure to use -hat to show which variable is being predicted.
Use these names for the variables: absences grade

Use the equation from #21 to predict the final grade for a student with 4 absences.
Round your answer to two places past the decimal point. No units necessary.

Using your answer from #22 for the predicted final grade and the actual final grade of 85.3 for a student with 4 absences, calculate the residual:
Enter your answer and tell if the model over or under predicted the final grade.

Would you feel comfortable using this model to predict the final grade for a student with 19 absences? Why or why not?
Think about what we discussed in class and what this is called.