Football owners are constantly in competition for good players. The more wins, the more possible that the team will provide good business returns for the owners. The resources that each of the 32 teams has in the National Football League (NFL) vary, but the draft system is designed to counteract the advantages that wealthier teams may have. Is it working or does the size of the payroll matter? Here is the regression output for the 2012/2013 season between the team payroll and the number of wins.

$$\begin{array}{lllll}\text { Predictor } & \text{Coeff} & \text{ SE(Coeff) } & \text{ t-Value}& \text{ P-Value}\end{array}$$

$$\begin{array}{lllll}\text { Intercept } & -16.31846 & 8.49246 & -1.922 & 0.06421\end{array}$$

$$\begin{array}{lrrrr}\text{ Payroll(\$M) } & 0.21875 & 0.07636 & 2.865 & 0.00755\end{array}$$

$$s=2.779 \quad R-S q=21.48 \%$$

b) Conduct the hypothesis test and state your conclusion in context.

How strong is the association between labor productivity and labor costs? Data from the Bureau of Labor Statistics for labor productivity, as measured by Output per Hour, and Unit Labor Costs across 53 industries, are used to examine this relationship (ftp://ftp.bls.gov; accessed June 2013).

a) From a scatterplot, is there evidence of a linear association between Labor Productivity and Unit Labor Costs? Plot the reciprocal, Hours per output $(000$ s), against Unit Labor Costs. Why did the analysts choose this measure of productivity?

A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. Here are their data:

Find the standard error of the regression slope. Show all three values that go into the calculation.

A training center, wishing to demonstrate the efficacy of its methods, tests some of its clients after different numbers of days of training, recording their scores on a sample test. Their data are:

The regression model they calculate is $\widehat{\text { Correct responses }}=3.525+0.525$ Training days.

c) Compute the residual standard deviation, $s_e$.

A training center, wishing to demonstrate the efficacy of its methods, tests some of its clients after different numbers of days of training, recording their scores on a sample test. Their data are:

The regression model they calculate is $\widehat{\text { Correct responses }}=3.525+0.525$ Training days.

b) What are the residuals, $e_i$?

A training center, wishing to demonstrate the efficacy of its methods, tests some of its clients after different numbers of days of training, recording their scores on a sample test. Their data are:

The regression model they calculate is $\widehat{\text { Correct responses }}=3.525+0.525$ Training days.

a) Employ the model to predict the correct responses for each number of training days.

A consumer organization has reported test data for 50 car models. We will investigate the association between the weight of the car (in thousands of pounds) and the fuel efficiency (in miles per gallon). Use the data provided on the disk to reply to the question, where the response variable is Fuel Efficiency (mpg).

d) Test the hypotheses and state your conclusion.

A consumer organization has reported test data for 50 car models. We will investigate the association between the weight of the car (in thousands of pounds) and the fuel efficiency (in miles per gallon). Use the data provided on the disk to reply to the question, where the response variable is Fuel Efficiency (mpg).

c) Write the suitable hypotheses for the slope.

A consumer organization has reported test data for 50 car models. We will investigate the association between the weight of the car (in thousands of pounds) and the fuel efficiency (in miles per gallon). Use the data provided on the disk to reply to the question, where the response variable is Fuel Efficiency (mpg).

b) Are the assumptions for regression satisfied?

A consumer organization has reported test data for 50 car models. We will investigate the association between the weight of the car (in thousands of pounds) and the fuel efficiency (in miles per gallon). Use the data provided on the disk to reply to the question, where the response variable is Fuel Efficiency (mpg).

a) Make the scatterplot and obtain the regression equation.

A business analyst looks at a company's assets and sales to determine the relationship (if any) between the two measures. She has data (in $million) from a random sample of 79 Fortune 500 companies and got the linear regression below:$

$$\begin{array}{lcccc}\text { Predictor } & \text { Coeff } & \text { SE(Coeff) } & \text { t-Ratio } & \text { P-Value } \\ \text { Intercept } & 1867.4 & 804.5 & 2.32 & 0.0230 \\ \text { Sales } & 0.975 & 0.099 & 9.84 & <0.0001\end{array}$$

$\mathrm{s}$=6132.59      $\mathrm{R}$-$\mathrm{Sq}$=55.7 %$Use the data provided to find a$95 \%$ confidence interval for the slope of the regression line and analyze your interval in context.

Classified ads in a newspaper offered many used Toyota Corollas for sale. The below Listed are the ages of the cars and the advertised prices.

If appropriate, create a$95 \%$ confidence interval for the slope of the regression line and explain what your interval means in context.

Classified ads in a newspaper offered many used Toyota Corollas for sale. The below Listed are the ages of the cars and the advertised prices.

d) Review the residuals to see if the conditions for inference are met.

Classified ads in a newspaper offered many used Toyota Corollas for sale. The below Listed are the ages of the cars and the advertised prices.

c) What is the equation of the regression line?