Day 8 Ch 6 & 7 Review

Last updated almost 5 years ago

24 questions

4

6

4

Library

Day 8 Ch 6 & 7 Review

Last updated almost 5 years ago

24 questions

4

All but one of the statements below contains a mistake. Which one could be true?

4

Which of the following are NOT conditions that must be checked before you can use correlation or calculate a linear regression?

4

The line of best fit is also called the least-squares regression line. Which statement best explains that name?

4

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

What are the roles of the variables and identify the slope/intercept.
Match to the correct answer.

Draggable item		Corresponding Item
		Explanatory Variable

4

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

Choose the best answers about the linear model above: (which answers are true?)
Choose two answers.

4

Use the data in the table below:

Enter the data into L1 & L2 of your calculator (Stat, Edit).
Create a scatterplot to check the three conditions.

Is it appropriate to proceed with a linear regression based on the scatterplot?

4

Use the equation from #7, predict how fast a motorboat with 200 HP can go.
Round your answer to 2 places past the decimal.

4

The motorboat from #8 is newer and built with aerodynamic and lightweight material.
The residual for max speed is 5.48 mph.
What is the actual max speed for this motorboat?

4

Use the information from questions #8 & 9 to explain the residual.

Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

4

Use the HP vs Max Speed of Motorboats.
Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.
1. What do you see?
2. What does this mean?

4

The day of the month and the amount in Kali's checking account were compared with a scatterplot and found to have a nearly linear relationship.
Use the residual plot below:

Which statement best assesses the linearity of the relationship between the day of the month and account balance if the scatterplot appears to be reasonably linear?

4

Use the scatterplots shown below. Which of the following is the strongest correlation?

Enter the correct letter.

4

Use the scatterplots shown below. Which of the following has the weakest correlation?

Enter the correct letter.

4

A study showed a strong negative correlation between the number of Blizzards sold at a local Dairy Queen and the number of cases of the flu.
Does this mean that eating Dairy Queen Blizzards prevents people from getting the flu? (is there a causal relationship here?)
If present, what could the possible lurking variable be?

4

In general, if the residual is positive what does this mean?

4

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

Enter the data into L1 & L2.
Calculate the correlation coefficient (r) by doing a linear regression (Stat, Calc, #8LinReg).

Round the correlation coefficient to three places past the decimal, enter it below.

4

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.
I will grade this question myself :)

4

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

The above data should be in L1 & L2 of the calculator.
Use the linear model created in # 20 to predict the annual repair costs for a car that has had 12 oil changes (one each month!).
Enter it below, round your answer to two places past the decimal.

6

4

Use the linear regression information from #20. Interpret the meaning of the slope (b).

Select the correct answer from the list below:

4

Use the linear regression information from #20. Interpret the meaning of the intercept (a).

Select the correct answer from the list below:

All but one of the statements below contains a mistake. Which one could be true?

Which of the following are NOT conditions that must be checked before you can use correlation or calculate a linear regression?

The line of best fit is also called the least-squares regression line. Which statement best explains that name?

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

What are the roles of the variables and identify the slope/intercept.
Match to the correct answer.

Draggable item		Corresponding Item
Number of hours		Explanatory Variable
Number of Bacteria		Response Variable
		Intercept, _____#bacteria
34,219		Slope, _____#bacteria/hour
532.1		Intercept, _______#hours

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

Choose the best answers about the linear model above: (which answers are true?)
Choose two answers.

Use the data in the table below:

Enter the data into L1 & L2 of your calculator (Stat, Edit).
Create a scatterplot to check the three conditions.

Is it appropriate to proceed with a linear regression based on the scatterplot?

Use the data in the table below:

Now that the data is in L1 & L2 of your calculator.
Do a linear regression to create the Linear Model: Stat, Calc, #8LinReg.
Select the correct linear model from the list below:

Use the equation from #7, predict how fast a motorboat with 200 HP can go.
Round your answer to 2 places past the decimal.

The motorboat from #8 is newer and built with aerodynamic and lightweight material.
The residual for max speed is 5.48 mph.
What is the actual max speed for this motorboat?

Use the information from questions #8 & 9 to explain the residual.

Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

Use the HP vs Max Speed of Motorboats.
Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.
1. What do you see?
2. What does this mean?

The day of the month and the amount in Kali's checking account were compared with a scatterplot and found to have a nearly linear relationship.
Use the residual plot below:

Which statement best assesses the linearity of the relationship between the day of the month and account balance if the scatterplot appears to be reasonably linear?

The least-squares regression equation:

cost-hat = 8.5 + 69.5(#phone lines)

can be used to predict the monthly cost(dollars) for cell phone service (with #phone lines).
The data point shows the number of phone lines and the actual cost.
(5, $350)

Calculate the residual to the nearest cent.
Hint: first calculate a prediction, then find the residual.

What does this mean?

Use the scatterplots shown below. Which of the following is the strongest correlation?

Enter the correct letter.

Use the scatterplots shown below. Which of the following has the weakest correlation?

Enter the correct letter.

A study showed a strong negative correlation between the number of Blizzards sold at a local Dairy Queen and the number of cases of the flu.
Does this mean that eating Dairy Queen Blizzards prevents people from getting the flu? (is there a causal relationship here?)
If present, what could the possible lurking variable be?

In general, if the residual is positive what does this mean?

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

Enter the data into L1 & L2.
Calculate the correlation coefficient (r) by doing a linear regression (Stat, Calc, #8LinReg).

Round the correlation coefficient to three places past the decimal, enter it below.

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.
I will grade this question myself :)

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

Now that the data is in L1 & L2, do another linear regression. This time SAVE THE RESIDUALS.
Stat, Calc, #8LinReg, 'Store RegEq', VARS, Y-VARS, enter, enter, enter, enter.

1. Check the residual plot, what do you see?
2. What does this mean about the appropriateness of using the linear model to make predictions?

Select BOTH correct answers.

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

The above data should be in L1 & L2 of the calculator.
Use the linear model created in # 20 to predict the annual repair costs for a car that has had 12 oil changes (one each month!).
Enter it below, round your answer to two places past the decimal.

Does the answer to #21 make sense?
Why might this have happened? (hint: what type of a prediction is this?)
Is this a wise thing to do?
Select THREE correct answers.

Use the linear regression information from #20. Interpret the meaning of the slope (b).

Select the correct answer from the list below:

Use the linear regression information from #20. Interpret the meaning of the intercept (a).

Select the correct answer from the list below:

The data must come from a random sample.

The histogram needs to show a roughly symmetric distribution.

The scatterplot must show a linear association.

The scatterplot cannot show any outliers.

Number of hours

Number of Bacteria

Response Variable

Intercept, _____#bacteria

34,219

Slope, _____#bacteria/hour

532.1

Intercept, _______#hours

This model produces (x,y) points of (number of bacteria, number of hours)

This model produces (x,y) points of (number of hours, number of bacteria)

No, the scatterplot does not show a linear relationship, there appear to be outliers.

No, the scatterplot shows a nearly linear relationship, no strong outliers and the data is quantitative.

Use the data in the table below:

Now that the data is in L1 & L2 of your calculator.
Do a linear regression to create the Linear Model: Stat, Calc, #8LinReg.
Select the correct linear model from the list below:

B

Random scatter on the residual plot.

A curved pattern reading from left to right.

A positive linear relationship reading from left to right.

The calculated linear model may not be appropriate for making predictions.

The calculatoed linear model is appropriate for making predictions.

Because the residual plot has no obvious pattern, and the scatterplot appears linear, it is appropriate to use the line of best fit to predict the account balance based on the day of the month.

Because the residual plot has no obvious pattern, and the scatterplot appears linear, it is not appropriate to use the line of best fit to predict the account balance based on the day of the month.

The least-squares regression equation:

cost-hat = 8.5 + 69.5(#phone lines) 

can be used to predict the monthly cost(dollars) for cell phone service (with #phone lines). 
The data point shows the number of phone lines and the actual cost.

(5, $350)

Calculate the residual to the nearest cent.
Hint: first calculate a prediction, then find the residual.

What does this mean?

The residual is $244.00 which means the linear model under predicted the monthly cost.

The residual is -$6.00 which means the linear model over predicted the monthly cost.

The residual is $244.00 which means the linear model over predicted the monthly cost.

The residual is $6.00 which means the linear model under predicted the monthly cost.

The residual is $6.00 which means the linear model over predicted the monthly cost.

The lurking variable could be cold weather, during cold weather cases of the flu increase and sales of Blizzards decrease.

Here is some fictional data for a set of cars showing the number of oil changes and the annual
cost of repairs for each car.

Now that the data is in L1 & L2, do another linear regression. This time SAVE THE RESIDUALS.
Stat, Calc, #8LinReg, 'Store RegEq', VARS, Y-VARS, enter, enter, enter, enter.

1. Check the residual plot, what do you see?
2. What does this mean about the appropriateness of using the linear model to make predictions?

Select BOTH correct answers.

The residual plot shows a definite pattern

The residudal plot shows a definite curve

The linear model is appropriate for making predictions due to the random scatter.

The residual plot shows random scatter

The linear model is not appropriate for making predictions due to the random scatter.

The linear model is appropriate for making predictions due to the pattern or curve.

The linear model is not appropriate for making predictions due to the pattern or curve.

Does the answer to #21 make sense?
Why might this have happened? (hint: what type of a prediction is this?)
Is this a wise thing to do?
Select THREE correct answers.

No, it does not make sense to have negative repair bills.

Yes, it does make sense to have negative repair bills because the car is very well taken care of.

Extrapolation is not a wise thing to do, predictions are risky.

Interpolation is an okay thing to do since the residual plot showed random scatter.

The prediction is within the range of the data values, it is called interpolation.

The prediction is beyond the range of the data values, it is called extrapolation.

For every additional oil change that is done on the car the annual repairs decrease by $73.07.

If no oil changes are done the annual repair cost is estimated to be $650.27

There is $650.27 spent on repairs for each oil change.

For every additional oil change that is done on the car the annual repairs decrease by $73.07.

If no oil changes are done the annual repair cost is estimated to be $650.27

Each oil change results in an additional $73.07 in repairs.

Day 8 Ch 6 & 7 Review

Day 8 Ch 6 & 7 Review

All but one of the statements below contains a mistake. Which one could be true?

Which of the following are NOT conditions that must be checked before you can use correlation or calculate a linear regression?

The line of best fit is also called the least-squares regression line. Which statement best explains that name?

Sally created the following model using data taken in an experiment in her laboratory.Number of Bacteria-hat = 34,219 + 532.1(hours)What are the roles of the variables and identify the slope/intercept.Match to the correct answer.

Sally created the following model using data taken in an experiment in her laboratory.Number of Bacteria-hat = 34,219 + 532.1(hours)Choose the best answers about the linear model above: (which answers are true?)Choose two answers.

Use the data in the table below:Enter the data into L1 & L2 of your calculator (Stat, Edit).Create a scatterplot to check the three conditions.Is it appropriate to proceed with a linear regression based on the scatterplot?

Use the equation from #7, predict how fast a motorboat with 200 HP can go.Round your answer to 2 places past the decimal.

The motorboat from #8 is newer and built with aerodynamic and lightweight material.The residual for max speed is 5.48 mph.What is the actual max speed for this motorboat?

Use the information from questions #8 & 9 to explain the residual.Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

Use the HP vs Max Speed of Motorboats.Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.1. What do you see?2. What does this mean?

Use the scatterplots shown below. Which of the following is the strongest correlation?Enter the correct letter.

Use the scatterplots shown below. Which of the following has the weakest correlation?Enter the correct letter.

In general, if the residual is positive what does this mean?

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.I will grade this question myself :)

Use the linear regression information from #20. Interpret the meaning of the slope (b).Select the correct answer from the list below:

Use the linear regression information from #20. Interpret the meaning of the intercept (a).Select the correct answer from the list below:

All but one of the statements below contains a mistake. Which one could be true?

Which of the following are NOT conditions that must be checked before you can use correlation or calculate a linear regression?

The line of best fit is also called the least-squares regression line. Which statement best explains that name?

Sally created the following model using data taken in an experiment in her laboratory.Number of Bacteria-hat = 34,219 + 532.1(hours)What are the roles of the variables and identify the slope/intercept.Match to the correct answer.

Sally created the following model using data taken in an experiment in her laboratory.Number of Bacteria-hat = 34,219 + 532.1(hours)Choose the best answers about the linear model above: (which answers are true?)Choose two answers.

Use the data in the table below:Enter the data into L1 & L2 of your calculator (Stat, Edit).Create a scatterplot to check the three conditions.Is it appropriate to proceed with a linear regression based on the scatterplot?

Use the data in the table below:Now that the data is in L1 & L2 of your calculator.Do a linear regression to create the Linear Model: Stat, Calc, #8LinReg.Select the correct linear model from the list below:

Use the equation from #7, predict how fast a motorboat with 200 HP can go.Round your answer to 2 places past the decimal.

The motorboat from #8 is newer and built with aerodynamic and lightweight material.The residual for max speed is 5.48 mph.What is the actual max speed for this motorboat?

Use the information from questions #8 & 9 to explain the residual.Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

Use the HP vs Max Speed of Motorboats.Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.1. What do you see?2. What does this mean?

Use the scatterplots shown below. Which of the following is the strongest correlation?Enter the correct letter.

Use the scatterplots shown below. Which of the following has the weakest correlation?Enter the correct letter.

In general, if the residual is positive what does this mean?

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.I will grade this question myself :)

Does the answer to #21 make sense?Why might this have happened? (hint: what type of a prediction is this?)Is this a wise thing to do?Select THREE correct answers.

Use the linear regression information from #20. Interpret the meaning of the slope (b).Select the correct answer from the list below:

Use the linear regression information from #20. Interpret the meaning of the intercept (a).Select the correct answer from the list below:

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

What are the roles of the variables and identify the slope/intercept.
Match to the correct answer.

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

Choose the best answers about the linear model above: (which answers are true?)
Choose two answers.

Use the data in the table below:

Enter the data into L1 & L2 of your calculator (Stat, Edit).
Create a scatterplot to check the three conditions.

Is it appropriate to proceed with a linear regression based on the scatterplot?

Use the equation from #7, predict how fast a motorboat with 200 HP can go.
Round your answer to 2 places past the decimal.

The motorboat from #8 is newer and built with aerodynamic and lightweight material.
The residual for max speed is 5.48 mph.
What is the actual max speed for this motorboat?

Use the information from questions #8 & 9 to explain the residual.

Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

Use the HP vs Max Speed of Motorboats.
Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.
1. What do you see?
2. What does this mean?

Use the scatterplots shown below. Which of the following is the strongest correlation?

Enter the correct letter.

Use the scatterplots shown below. Which of the following has the weakest correlation?

Enter the correct letter.

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.
I will grade this question myself :)

Use the linear regression information from #20. Interpret the meaning of the slope (b).

Select the correct answer from the list below:

Use the linear regression information from #20. Interpret the meaning of the intercept (a).

Select the correct answer from the list below:

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

What are the roles of the variables and identify the slope/intercept.
Match to the correct answer.

Sally created the following model using data taken in an experiment in her laboratory.

Number of Bacteria-hat = 34,219 + 532.1(hours)

Choose the best answers about the linear model above: (which answers are true?)
Choose two answers.

Use the data in the table below:

Enter the data into L1 & L2 of your calculator (Stat, Edit).
Create a scatterplot to check the three conditions.

Is it appropriate to proceed with a linear regression based on the scatterplot?

Use the data in the table below:

Now that the data is in L1 & L2 of your calculator.
Do a linear regression to create the Linear Model: Stat, Calc, #8LinReg.
Select the correct linear model from the list below:

Use the equation from #7, predict how fast a motorboat with 200 HP can go.
Round your answer to 2 places past the decimal.

The motorboat from #8 is newer and built with aerodynamic and lightweight material.
The residual for max speed is 5.48 mph.
What is the actual max speed for this motorboat?

Use the information from questions #8 & 9 to explain the residual.

Did the linear model (equation) overpredict or underpredict the max speed of the motorboat?

Use the HP vs Max Speed of Motorboats.
Now check the residual plot for the HP vs Max Speed to determine if it is appropriate to continue using the Linear Model to make predictions.
1. What do you see?
2. What does this mean?

Use the scatterplots shown below. Which of the following is the strongest correlation?

Enter the correct letter.

Use the scatterplots shown below. Which of the following has the weakest correlation?

Enter the correct letter.

Describe the relationship between the number of oil changes and the annual cost of repairs for a car.
I will grade this question myself :)

Does the answer to #21 make sense?
Why might this have happened? (hint: what type of a prediction is this?)
Is this a wise thing to do?
Select THREE correct answers.

Use the linear regression information from #20. Interpret the meaning of the slope (b).

Select the correct answer from the list below:

Use the linear regression information from #20. Interpret the meaning of the intercept (a).

Select the correct answer from the list below: