Day 8 Ch 6 & 7 Review

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.

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 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:

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

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?

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.

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.

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

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.

4

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

Select the correct answer from the list below:

4

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 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: