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Business Statistics: A First Course, Global Edition

David Levine, Kathryn Szabat, David Stephan

Chapter 12

Simple Linear Regression - all with Video Answers

Educators


Chapter Questions

03:55

Problem 1

Fitting a straight line to a set of data yields the following prediction line:
$$
\hat{Y}_i=7+2 X_i
$$
a. Interpret the meaning of the $Y$ intercept, $b_0$ -
b. Interpret the meaning of the slope, $b_1$.
c. Predict the value of $Y$ for $X=3$.

Kayleah Tsai
Kayleah Tsai
Numerade Educator
00:52

Problem 2

Identify which of the following is an interpolation or extrapolation prediction if the values $X$ in Problem 12.1 range from 11 to 38 :
a. 10
b. 25
c. 33
d. 41

Mrinal Rana
Mrinal Rana
Numerade Educator
02:33

Problem 3

Fitting a straight line to a set of data yields to the following prediction line:
$$
\hat{Y}_i=1.01-0.02 X_i
$$
a. Interpret the meaning of the $Y$ intercept, $b_0$ -
b. Interpret the meaning of the slope, $b_1$.
c. Find the value of $Y$ when $X=88$.

Carson Merrill
Carson Merrill
Numerade Educator
05:38

Problem 4

A car's power output is primarily influenced by the size of its engine. A sample of 90 different car models of three makes from the European market is stored in the file Cars. Develop a simple regression model to predict power output (kW), based on engine size (displacement, in cubic centimeters).
Source: Data extracted from bit.ly/2EAWEHF.
a. Construct a scatter plot.
For these data, $b_0=-44.878$ and $b_1=0.081$.
b. Interpret the meaning of the slope, $b_1$, in this problem.
c. Predict the mean power output for cars with a displacement of $2,000 \mathrm{~cm}^3$.
d. What conclusion can you reach based on the results of (a)-(c)?

Teresa Wray
Teresa Wray
Numerade Educator
03:15

Problem 5

Zagat's publishes restaurant ratings for various locations in the United States. The file Restaurants contains the Zagat rating for food, décor, service, and the cost per person for a sample of 100 restaurants located in the center of New York City and in an outlying area of New York City. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the ratings for food, décor, and service.
a. Construct a scatter plot.
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
d. Predict the mean cost per person for a restaurant with a summated rating of 50 .
e. What should you tell the owner of a group of restaurants in this geographical area about the relationship between the summated rating and the cost of a meal?

Sheryl Ezze
Sheryl Ezze
Numerade Educator
04:01

Problem 6

Is an MBA a golden ticket? Pursuing an MBA is a major personal investment. Tuition and expenses associated with business school programs are costly, but the high costs come with hopes of career advancement and high salaries. A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program peryear tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The data are stored in FTMBA.
a. Construct a scatter plot.
b. Assuming a linear relationship, use the least-squares method to determine the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of the slope, $b_1$, in this problem.
d. Predict the mean starting salary upon graduation for a program that has a per-year tuition cost of $$\$ 50,450$$.
e. What insights do you gain about the relationship between program per-year tuition and starting salary upon graduation?

Nick Johnson
Nick Johnson
Numerade Educator

Problem 7

Starbucks Coffee Co. uses a data-based approach to improve the quality and customer satisfaction of its products. When survey data indicated that Starbucks needed to improve its package-sealing process, an experiment was conducted to determine the factors in the bag-sealing equipment that might be affecting the ease of opening the bag without tearing the inner liner of the bag.
One factor that could affect the rating of the ability of the bag to resist tears was the plate gap on the bag-sealing equipment. Data were collected on 19 bags in which the plate gap was varied. The results are stored in Starbucks.
a. Construct a scatter plot.
b. Assuming a linear relationship, use the least-squares method to determine the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of the slope, $b_1$, in this problem.
d. Predict the mean tear rating when the plate gap is equal to 0 .
e. What should you tell management of Starbucks about the relationship between the plate gap and the tear rating?

Check back soon!
05:25

Problem 8

The file Internet contains data about internet users and Facebook users as of December 31,2017, based on a sample of 40 Asian and African countries. Suppose you want to develop a simple linear regression model to predict the number of Facebook users based on the number of internet users of a country (both measured in million persons).
a. Construct a scatter plot.
b. Use the least-squares method to determine the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of $b_0$ and $b_1$ in this problem.
d. Predict the mean number of Facebook users of a country that has 20.5 million internet users.
e. What should you conclude about the relationship between internet users and Facebook users in the countries?

Willis James
Willis James
Numerade Educator
01:42

Problem 9

An agent for a residential real estate company in a suburb located outside of Washington, DC, has the business objective of developing more accurate estimates of the monthly rental cost for apartments. Toward that goal, the agent would like to use the size of an apartment, as defined by square footage to predict the monthly rental cost. The agent selects a sample of 57 one-bedroom apartments and collects and stores the data in RentSilverSpring.
a. Construct a scatter plot.
b. Use the least-squares method to determine the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of $b_0$ and $b_1$ in this problem.
d. Predict the mean monthly rent for an apartment that has 800 square feet.
e. Why would it not be appropriate to use the model to predict the monthly rent for apartments that have 1,500 square feet?
f. Your friends Jim and Jennifer are considering signing a lease for a one-bedroom apartment in this residential neighborhood. They are trying to decide between two apartments, one with 800 square feet for a monthly rent of $$\$ 1,130$$ and the other with 830 square feet for a monthly rent of $$\$ 1,410$$. Based on (a) through (d), which apartment do you think is a better deal?

Sheryl Ezze
Sheryl Ezze
Numerade Educator
02:46

Problem 10

A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use YouTube trailer views as a predictor. For each of 66 movies, the YouTube trailer view count, the number of YouTube trailer views from the release of the trailer through the Saturday before a movie opens, and the opening weekend box office gross (in $$\$ $$millions) are collected and stored in Movie.

For these data,
a. Construct a scatter plot.
b. Assuming a linear relationship, use the least-squares method to determine the regression coefficients $b_0$ and $b_1$.
c. Interpret the meaning of the slope, $b_1$, in this problem.
d. Predict the mean weekend box office gross for a movie that had 20 million YouTube trailer views.
e. What conclusions can you reach about predicting weekend box office gross from YouTube trailer views?

Shu Naito
Shu Naito
Numerade Educator
03:12

Problem 11

How do you interpret a coefficient of determination, $r^2$, equal to 0.14 ?

Jameson Kuper
Jameson Kuper
Numerade Educator
02:15

Problem 12

If $S S R=0.713$ and $S S E=0.037$, determine $S S T$ and then compute the coefficient of determination, $r^2$, and interpret its meaning.

Jameson Kuper
Jameson Kuper
Numerade Educator
01:42

Problem 13

If $S S R=29$ and $S S T=104$ for a set of 17 observations, determine the standard error of the estimate, $S_{X Y}$, and interpret its meaning.

Sheryl Ezze
Sheryl Ezze
Numerade Educator
02:15

Problem 14

If $S S E=3,412$ and $S S R=9,354$, compute the coefficient of determination, $r^2$, and interpret its meaning.

Jameson Kuper
Jameson Kuper
Numerade Educator
05:47

Problem 15

If $S S R=235$, what is the minimum value of $S S T$ ?

Patrick Hall
Patrick Hall
Numerade Educator
01:30

Problem 16

In Problem 12.4 on page 489 , the engine size TEST (displacement) was used to predict power output of the cars (stored in Cars). For those data, $S S R=213,502.341$ and $S S T=338,784.114$.
a. Determine the coefficient of determination, $r^2$, and interpret its meaning.
b. Determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting power output of cars?

Shu Naito
Shu Naito
Numerade Educator

Problem 17

In Problem 12.5 on page 489, you used the summated rating to predict the cost of a restaurant meal (stored in Restaurants)
a. Determine the coefficient of determination, $r^2$, and interpret its meaning.
b. Determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting the cost of a restaurant meal?

Check back soon!
01:27

Problem 18

In Problem 12.6 on page 490, a prospective MBA student wanted to predict starting salary upon graduation, based on program per-year tuition (stored in FTMBA). Using the results of that problem, a. determine the coefficient of determination, $r^2$, and interpret its meaning.
b. determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting starting salary?

Adriano Chikande
Adriano Chikande
Numerade Educator
00:44

Problem 19

In Problem 12.7 on page 490, you used the plate gap on the bag-sealing equipment to predict the tear rating of a bag of coffee (stored in Starbucks). Using the results of that problem,
a. determine the coefficient of determination, $r^2$, and interpret its meaning.
b. determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting the tear rating based on the plate gap in the bag-sealing equipment?

Sheryl Ezze
Sheryl Ezze
Numerade Educator
01:45

Problem 20

In Problem 12.8 on page 490, you used the internet users to predict Facebook users by countries (stored in Internot). Using the results of that problem,
a. determine the coefficient of determination, $r^2$, and interpret its meaning.
b. determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting the number of Facebook users?

Carson Merrill
Carson Merrill
Numerade Educator
02:43

Problem 21

In Problem 12.9 on page 490, an agent for a real estate company wanted to predict the monthly rent for one-bedroom apartments, based on the size of the apartment (stored in RentsilverSpring ). Using the results of that problem,
a. determine the coefficient of determination, $r^2$, and interpret its meaning.
b. determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting the monthly rent?
d. Can you think of other variables that might explain the variation in monthly rent?

Sarah Wharton
Sarah Wharton
Numerade Educator
View

Problem 22

In Problem 12.10 on page 490, you used YouTube trailer views to predict movie weekend box office gross (stored in Movie ). Using the results of that problem,
a. determine the coefficient of determination, $r^2$, and interpret its meaning.
b. determine the standard error of the estimate.
c. How useful do you think this regression model is for predicting movie weekend box office gross?
d. Can you think of other variables that might explain the variation in movie weekend box office gross?

Ana Carolina Da Cruz
Ana Carolina Da Cruz
Numerade Educator
03:13

Problem 23

The following graph presents a residual plot from a regression analysis for the standardized residuals versus the $X$ values.
(GRAPH CANT COPY)
Interpret the graph.

Sophie Knight
Sophie Knight
Numerade Educator
03:13

Problem 24

Consider the following graphs of the scatterplot for variable $Y$ versus $X$, the histogram of the standardized residuals, the residuals versus $X$ plot for a given set of data $X$ and $Y$.
(GRAPH CANT COPY)
(GRAPH CANT COPY)
Justify whether each model is appropriate for the given set of data, $X$ and $Y$, by interpreting each plot.

Sophie Knight
Sophie Knight
Numerade Educator
01:30

Problem 25

In Problem 12.5 on page 489 , you used the summated rating to predict the cost of a restaurant meal. Perform a residual analysis for these data (stored in Restaurants ). Evaluate whether the assumptions of regression have been seriously violated. engine size to predict power output of cars. Perform a residual analysis for these data (stored in Cars). Evaluate whether the assumptions of regression have been seriously violated.

Shu Naito
Shu Naito
Numerade Educator
05:38

Problem 26

In Problem 12.4 on page 489 , you used the TEST engine size to predict power output of cars. Perform a residual analysis for these data (stored in Cars). Evaluate whether the assumptions of regression have been seriously violated.

Teresa Wray
Teresa Wray
Numerade Educator
01:28

Problem 27

In Problem 12.7 on page 490, you used the plate gap on the bag-sealing equipment to predict the tear rating of a bag of coffee. Perform a residual analysis for these data (stored in Starbucks). Based on these results, evaluate whether the assumptions of regression have been seriously violated.

Sheryl Ezze
Sheryl Ezze
Numerade Educator
01:28

Problem 28

In Problem 12.6 on page 490, a prospective MBA student wanted to predict starting salary upon graduation, based on program per-year tuition. Perform a residual analysis for these data (stored in FTMBA ). Based on these results, evaluate whether the assumptions of regression have been seriously violated.

Sheryl Ezze
Sheryl Ezze
Numerade Educator
02:43

Problem 29

In Problem 12.9 on page 490, an agent for a real estate company wanted to predict the monthly rent for one-bedroom apartments, based on the size of the apartments. Perform a residual analysis for these data (stored in RentSilverSpring). Based on these results, evaluate whether the assumptions of regression have been seriously violated.

Sarah Wharton
Sarah Wharton
Numerade Educator
02:46

Problem 30

In Problem 12.8 on page 490, you used the internet users to predict Facebook users by countries (stored in Internet). Based on these results, evaluate whether the assumptions of regression have been seriously violated.

James Kiss
James Kiss
Numerade Educator
View

Problem 31

In Problem 12.10 on page 490, you used YouTube trailer views to predict movie weekend box office gross. Perform a residual analysis for these data (stored in Movie). Based on these results, evaluate whether the assumptions of regression have been seriously violated.

Ana Carolina Da Cruz
Ana Carolina Da Cruz
Numerade Educator
01:40

Problem 32

The residuals for 10 consecutive time periods are as follows:
$$
\begin{array}{cc|cc}
\text { Time Period } & \text { Residual } & \text { Time Period } & \text { Residual } \\
\hline 1 & -0.038 & 6 & -0.013 \\
2 & -0.036 & 7 & -0.004 \\
3 & -0.032 & 8 & -0.002 \\
4 & -0.019 & 9 & 0.004 \\
5 & -0.017 & 10 & 0.023
\end{array}
$$
a. Construct a scatter plot for the residuals over time. What is the purpose of constructing such a graph?
b. Based on (a), what conclusion can you reach about the pattern of the residuals over time and about the autocorrelation of the residuals?

Shu Naito
Shu Naito
Numerade Educator
08:26

Problem 33

The residuals for 16 consecutive time periods are as follows:
$$
\begin{array}{cc|cc}
\text { Time Period } & \text { Residuals } & \text { Time Period } & \text { Residuals } \\
\hline 1 & 0.15 & 9 & -0.09 \\
2 & 0.09 & 10 & -0.08 \\
3 & 0.04 & 11 & -0.07 \\
4 & 0.00 & 12 & -0.06 \\
5 & -0.03 & 13 & -0.03 \\
6 & -0.06 & 14 & 0.00 \\
7 & -0.07 & 15 & 0.04 \\
8 & -0.09 & 16 & 0.06
\end{array}
$$
a. Construct a scatter plot for the given data. What conclusion can you reach about the pattern of the residuals over time?
b. Compute the Durbin-Watson statistic. At the 0.05 level of significance, is there evidence of positive autocorrelation among the residuals?
c. Based on (a) and (b), what conclusion can you reach about the autocorrelation of the residuals?

Sriparna Bhattacharjee
Sriparna Bhattacharjee
Numerade Educator

Problem 34

In Problem 12.7 on page 490 conceming the bag-sealing equipment at Starbucks, you used the plate gap to predict the tear rating.
a. Is it necessary to compute the Durbin-Watson statistic in this case? Explain.
b. Under what circumstances is it necessary to compute the DurbinWatson statistic before proceeding with the least-squares method of regression analysis?

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02:02

Problem 35

What is the relationship between the price of crude oil and the price you pay at the pump for gasoline? The file Oil \& Gasoline contains the price ($$\$ $$) for a barrel of crude oil (Cushing, Oklahoma, spot price) and a gallon of gasoline (U.S. average conventional spot price) for 388 weeks, ending June 2, 2017.
Source: Data extracted from www.eia.gov.
a. Construct a scatter plot with the price of oil on the horizontal axis and the price of gasoline on the vertical axis.
b. Use the least-squares method to develop a simple linear regression equation to predict the price of a gallon of gasoline using the price of a barrel of crude oil as the independent variable.
c. Interpret the meaning of the slope, $b_1$, in this problem.
d. Plot the residuals versus the time period.
e. Compute the Durbin-Watson statistic.
f. At the 0.05 level of significance, is there evidence of positive autocorrelation among the residuals?
g. Based on the results of (d) through (f), is there reason to question the validity of the model?
h. What conclusions can you reach concerning the relationship between the price of a barrel of crude oil and the price of a gallon of gasoline?

Erika Bustos
Erika Bustos
Numerade Educator
00:45

Problem 36

A mail-order catalog business that sells personal computer supplies, software, and hardware maintains a centralized warehouse for the distribution of products ordered. Management is currently examining the process of distribution from the warehouse and has the business objective of determining the factors that affect warehouse distribution costs. Currently, a handling fee is added to the order, regardless of the amount of the order. Data that indicate the warehouse distribution costs and the number of orders received have been collected over the past 24 months and are stored in Warecost.
a. Assuming a linear relationship, use the least-squares method to find the regression coefficients $b_0$ and $b_1$.
b. Predict the monthly warehouse distribution costs when the number of orders is 4,500 .
c. Plot the residuals versus the time period.
d. Compute the Durbin-Watson statistic. At the 0.05 level of significance, is there evidence of positive autocorrelation among the residuals?
e. Based on the results of (c) and (d), is there reason to question the validity of the model?
f. What conclusions can you reach concerning the factors that affect distribution costs?

Maxime Rossetti
Maxime Rossetti
Numerade Educator
01:32

Problem 37

A freshly brewed shot of espresso has three distinct components: the heart, body, and crema. The separation of these three components typically lasts only 10 to 20 seconds. To use the espresso shot in making a latte, a cappuccino, or another drink, the shot must be poured into the beverage during the separation of the heart, body, and crema. If the shot is used after the separation occurs, the drink becomes excessively bitter and acidic, ruining the final drink. Thus, a longer separation time allows the drink-maker more time to pour the shot and ensure that the beverage will meet expectations. An employee at a coffee shop hypothesized that the harder the espresso grounds were tamped down into the portafilter before brewing, the longer the separation time would be. An experiment using 24 observations was conducted to test this relationship. The independent variable Tamp measures the distance, in inches, between the espresso grounds and the top of the portafilter (i.e., the harder the tamp, the greater the distance). The dependent variable Time is the number of seconds the heart, body, and crema are separated (i.e., the amount of time after the shot is poured before it must be used for the customer's beverage). The data are stored in Espresso.
a. Use the least-squares method to develop a simple regression equation with Time as the dependent variable and Tamp as the independent variable.
b. Predict the separation time for a tamp distance of 0.50 inch.
c. Plot the residuals versus the time order of experimentation. Are there any noticeable patterns?
d. Compute the Durbin-Watson statistic. At the 0.05 level of significance, is there evidence of positive autocorrelation among the residuals?
e. Based on the results of (c) and (d), is there reason to question the validity of the model?
f. What conclusions can you reach concerning the effect of tamping on the time of separation?

Aadit Sharma
Aadit Sharma
Numerade Educator

Problem 38

The owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized during the summer season. As a starting point, the owners decide to develop a simple linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days and store the results in IceCream.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Predict the sales for a day in which the temperature is $81^{\circ} \mathrm{F}$.
c. Plot the residuals versus the time period.
d. Compute the Durbin-Watson statistic. At the 0.05 level of significance, is there evidence of positive autocorrelation among the residuals?
e. Based on the results of (c) and (d), is there reason to question the validity of the model?

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03:11

Problem 39

You are testing the null hypothesis that there is no linear relationship between two variables, $X$ and $Y$. From your sample of $n=9$, you determine that $r=0.80$.
a. What is the value of the $t$ test statistic $t_{\text {STAT }}$ ?
b. At the $\alpha=0.05$ level of significance, what are the critical values?
c. Based on your answers to (a) and (b), what statistical decision should you make?

Willis James
Willis James
Numerade Educator
06:35

Problem 40

You are testing the null hypothesis that there is no linear relationship between two variables, $X$ and $Y$. From your sample of $n=7$, you determine that $b_1=-835.72$ and $S_{b_1}=99.65$.
a. What is the value of $t_{\text {STAT }}$ ?
b. At the $\alpha=0.05$ level of significance, what are the critical values?
c. Based on your answers to (a) and (b), what statistical decision should you make?
d. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

Willis James
Willis James
Numerade Educator

Problem 41

A simple linear regression analysis determines $\operatorname{cov}(X, Y)=$ $110.9118, S_X=50.4975$, and the $S_Y=2.3361$ for a set of 17 observations. Answer the following based on the given information;
a. Compute the correlation of coefficient value.
b. Test the null and alternative hypothesis for a $t$ test of correlation coefficient to determine whether there is a correlation between the $X$ and $Y$ variable.
c. Based on your answer to (b), what statistical decision should you make for the $t$ test of correlation coefficient at a 0.05 level of significance?
d. What is the value of $t_{S T A T}$ ?
e. Based on your answers to (a) through (d), what conclusion would you reach?

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04:28

Problem 42

In Problem 12.4 on page 490 you used the engine size to predict power output of cars. Perform a residual analysis for these data (stored in Cars ). From the results of that problem, $b_1=0.081$ and $S_{b_1}=0.0066$.
a. At the 0.05 level of significance, is there evidence of a linear relationship between the displacement and power output of cars?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

Jameson Kuper
Jameson Kuper
Numerade Educator
01:13

Problem 43

In Problem 12.5 on page 489, you used the summated rating of a restaurant to predict the cost of a meal. The data are stored in Restaurants .
a. At the 0.05 level of significance, is there evidence of a linear relationship between the summated rating of a restaurant and the cost of a meal?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

Supriya Kulkarni
Supriya Kulkarni
Numerade Educator

Problem 44

In Problem 12.6 on page 490, a prospective MBA student wanted to predict starting salary upon graduation, based on program per-year tuition. The data are stored in FTMBA. Use the results of that problem.
a. At the 0.05 level of significance, is there evidence of a linear relationship between the starting salary upon graduation and program per-year tuition?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

Check back soon!

Problem 45

In Problem 12.7 on page 490 , used the plate gap in the bag-sealing equipment to predict the tear rating of a bag of coffee. The data are stored in Starbucks. Use the results of that problem.
a. At the 0.05 level of significance, is there evidence of a linear relationship between the plate gap of the bag-sealing machine and the tear rating of a bag of coffee?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

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00:43

Problem 46

In Problem 12.8 on page 490, you used the internet users to predict Facebook users by countries. The data are stored in Internet. Use the results of that problem.
a. At the 0.05 level of significance, is there evidence of a linear relationship between the internet users and Facebook users?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

Madysn Cardinal
Madysn Cardinal
Numerade Educator

Problem 47

In Problem 12.9 on page 490, an agent for a real estate company wanted to predict the monthly rent for one-bedroom apartments, based on the size of the apartment. The data are stored in RentSilverSpring. Use the results of that problem.
a. At the 0.05 level of significance, is there evidence of a linear relationship between the size of the apartment and the monthly rent?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

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Problem 48

In Problem 12.10 on page 490, you used YouTube trailer views to predict movie weekend box office gross from data stored in Movie. Use the results of that problem.
a. At the 0.05 level of significance, is there evidence of a linear relationship between YouTube trailer views and movie weekend box office gross?
b. Construct a $95 \%$ confidence interval estimate of the population slope, $\beta_1$.

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01:12

Problem 49

The volatility of a stock is often measured by its beta value. You can estimate the beta value of a stock by developing a simple linear regression model, using the percentage weekly change in the stock as the dependent variable and the percentage weekly change in a market index as the independent variable. The S\&P 500 Index is a common index to use. For example, if you wanted to estimate the beta value for Disney, you could use the following model, which is sometimes referred to as a market model:
$$
\begin{aligned}
& \text { \% weekly change in Disney }=\beta_0 \\
& \quad+\beta_1 \text { (percent weekly change in S\&P } 500 \text { index) }+\varepsilon
\end{aligned}
$$
The least-squares regression estimate of the slope $b_1$ is the estimate of the beta value for Disney. A stock with a beta value of 1.0 tends to move the same as the overall market. A stock with a beta value of 1.5 tends to move $50 \%$ more than the overall market, and a stock with a beta value of 0.6 tends to move only $60 \%$ as much as the overall market. Stocks with negative beta values tend to move in the opposite direction of the overall market. The following table gives some beta values for some widely held stocks as of April 23, 2018.
$$
\begin{array}{lcc}
\text { Company } & \text { Ticker Symbol } & \text { Beta } \\
\hline \text { Apple } & \text { AAPL } & 1.10 \\
\text { Disney } & \text { DIS } & 1.18 \\
\text { American Eagle Mines } & \text { AEM } & 0.20 \\
\text { Marriott } & \text { MAR } & 1.32 \\
\text { Microsoft } & \text { MSFT } & 1.28 \\
\text { Procter \& Gamble } & \text { PG } & 0.38
\end{array}
$$
a. For each of the six companies, interpret the beta value.
b. How can investors use the beta value as a guide for investing?

Dominador Tan
Dominador Tan
Numerade Educator
07:29

Problem 50

Index funds are mutual funds that try to mimic the movement of leading indexes, such as the S\&P 500 or the Russell 2000. The beta values (as described in Problem 12.49) for these funds are therefore approximately 1.0 , and the estimated market models for these funds are approximately
$$
\begin{gathered}
\% \text { weekly change in index fund }=0.0 \\
+1.0(\% \text { weekly change in the index })
\end{gathered}
$$
Leveraged index funds are designed to magnify the movement of major indexes. Direxion Funds is a leading provider of leveraged index and other alternative-class mutual fund products for investment advisors and sophisticated investors. Two of the company's funds are shown in the following table:
$$
\begin{array}{lcc}
\text { Name } & \text { Ticker Symbol } & \text { Description } \\
\hline \text { Daily Small Cap } & \text { TNA } & 300 \% \text { of the Russell } \\
\text { Bull 3x Fund } & & 2000 \text { Index } \\
\text { Daily S\&P 500 } & \text { SPUU } & 200 \% \text { of the S\&P } \\
\text { Bull 2x Fund } & & 500 \text { Index }
\end{array}
$$
The estimated market models for these funds are approximately
$\%$ daily change in TNA $=0.0$
+3.0 (\% daily change in the Russell 2000)
$\%$ daily change in SPUU $=0.0$
+2.0 (\% daily change in the S\&P 500 Index)
Thus, if the Russell 2000 Index gains $10 \%$ over a period of time, the leveraged mutual fund TNA gains approximately $30 \%$. On the downside, if the same index loses $20 \%$, TNA loses approximately $60 \%$.
a. The objective of the Direxion Funds Bull $2 x$ Fund, SPUU, is $200 \%$ of the performance of the S\&P 500 Index. What is its approximate market model?
b. If the S\&P 500 Index gains $10 \%$ in a year, what return do you expect SPUU to have?
c. If the S\&P 500 Index loses $20 \%$ in a year, what return do you expect SPUU to have?
d. What type of investors should be attracted to leveraged index funds? What type of investors should stay away from these funds?

Dominador Tan
Dominador Tan
Numerade Educator
02:19

Problem 51

The file CoffeeDrink contains the calories and fat, in grams, of seven different types of coffee drinks:
$$
\begin{array}{ccc}
\text { Coffee Drink } & \text { Calories } & \text { Fat } \\
\hline 1 & 238 & 7.0 \\
2 & 259 & 3.4 \\
3 & 346 & 22.2 \\
4 & 347 & 19.8 \\
5 & 419 & 16.3 \\
6 & 505 & 21.5 \\
7 & 527 & 18.7
\end{array}
$$
a. Compute and interpret the coefficient of correlation, $r$.
b. At the 0.05 level of significance, is there a significant linear relationship between calories and fat?

James Kiss
James Kiss
Numerade Educator
02:20

Problem 52

Movie companies need to predict the gross receipts of an individual movie once the movie has debuted. The following results (stored in PotterMovies) are the first weekend gross, the U.S. gross, and the worldwide gross (in $$\$ $$millions) of the eight Harry Potter movies that debuted from 2001 to 2011 :
$$
\begin{array}{lrcr}
\text { Title } & \begin{array}{c}
\text { First } \\
\text { Weekend }
\end{array} & \begin{array}{c}
\text { U.S. } \\
\text { Gross }
\end{array} & \begin{array}{c}
\text { Worldwide } \\
\text { Gross }
\end{array} \\
\hline \text { Sorcerer's Stone } & 90.295 & 317.558 & 976.458 \\
\text { Chamber of Secrets } & 88.357 & 261.988 & 878.988 \\
\text { Prisoner of Azkaban } & 93.687 & 249.539 & 795.539 \\
\text { Goblet of Fire } & 102.335 & 290.013 & 896.013 \\
\text { Order of the Phoenix } & 77.108 & 292.005 & 938.469 \\
\text { Half-Blood Prince } & 77.836 & 301.460 & 934.601 \\
\text { Deathly Hallows Part I } & 125.017 & 295.001 & 955.417 \\
\text { Deathly Hallows Part II } & 169.189 & 381.001 & 1,328.11
\end{array}
$$
a. Compute the coefficient of correlation between first weekend gross and U.S. gross, first weekend gross and worldwide gross, and U.S. gross and worldwide gross.
b. At the 0.05 level of significance, is there a significant linear relationship between first weekend gross and U.S. gross, first weekend gross and worldwide gross, and U.S. gross and worldwide gross?

Maxime Rossetti
Maxime Rossetti
Numerade Educator
09:36

Problem 53

The file MobileSpeed contains the overall download and upload speeds in mbps for nine carriers in the United States. Source: Data extracted from "Best Mobile Network 2016," bit.ly/1KGPrMm, accessed November 10, 2016.
a. Compute and interpret the coefficient of correlation, $r$.
b. At the 0.05 level of significance, is there a significant linear relationship between download and upload speed?
12.54 The file Transportation Fuels Production and Demand (1993-2018) contains the weekly production volume and weekly demand for gasoline in the United States. The data is reported weekly starting in July 1993 through till end of December 2018.
Source: Data extracted from "Transportation Fuels Production and Demand: Beginning 1993," https://bit.ly/2WIDmra.
a. Compute the coefficient of correlation, $r$.
b. At the 0.01 level of significance, is there a significant linear relationship between the production and the demand for gasoline?
c. Compute and interpret the coefficient of determination, $r^2$.

OC
Omer Ceyhan
Numerade Educator
04:24

Problem 55

Based on a sample of $n=29$, the least-squares method was used to develop the following prediction line: $\hat{Y}_i=5+3 X_{i-}$ In addition,
$$
S_{Y X}=2.3, \bar{X}=10, \text { and } \sum_{i=1}^n\left(X_i-\bar{X}\right)^2=29
$$
a. Construct a $90 \%$ confidence interval estimate of the population mean response for $X=5$.
b. Construct a $90 \%$ prediction interval of an individual response for $X=5$.

Lucas Finney
Lucas Finney
Numerade Educator
02:57

Problem 56

Based on a sample of $n=20$, the least-squares method was used to develop the following prediction line: $\hat{Y}_i=-0.2977+$ $0.0466 X_i$.
In addition,
$$
\bar{X}=90 \quad S_{X Y}=0.6754 \quad S S X=178,500
$$
a. Calculate the value for $\hat{Y}_i$ when $X_i=85$.
b. Construct a $95 \%$ confidence interval estimate of the population mean response for an individual response, $Y$.
c. Interpret the confidence interval from part (b).

James Kiss
James Kiss
Numerade Educator
01:13

Problem 57

In Problem 12.5 on page 489 , you used the summated rating of a restaurant to predict the cost of a meal. The data are stored in Restaurants .
a. Construct a $95 \%$ confidence interval estimate of the mean cost of a meal for restaurants that have a summated rating of 50 .
b. Construct a $95 \%$ prediction interval of the cost of a meal for an individual restaurant that has a summated rating of 50 .
c. Explain the difference in the results in (a) and (b).

Supriya Kulkarni
Supriya Kulkarni
Numerade Educator

Problem 58

In Problem 12.4 on page 489, you used the TEST engine size to predict power output of cars. Perform a residual analysis for these data (stored in Cars ). For these data, $S_{Y X}=37.7314$ and $h_i=0.0163$ when $X=1,590$.
a. Construct a $95 \%$ confidence interval estimate of the mean power output for all cars that have $1,590 \mathrm{~cm}^3$ displacement.
b. Construct a $95 \%$ prediction interval of the power output of an individual car that has $1,590 \mathrm{~cm}^3$ displacement.
c. Explain the difference in the results in (a) and (b).

c. Construct and interpret the interval you selected in (b).

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View

Problem 59

In Problem 12.7 on page 490, you used the plate gap on the bag-sealing equipment to predict the tear rating of a bag of coffee. The data are stored in Starbucks.
a. Construct a $95 \%$ confidence interval estimate of the mean tear rating for all bags of coffee when the plate gap is 0 .
b. Construct a $95 \%$ prediction interval of the tear rating for an individual bag of coffee when the plate gap is 0 .
c. Why is the interval in (a) narrower than the interval in (b)?

Rashmi Sinha
Rashmi Sinha
Numerade Educator

Problem 60

In Problem 12.6 on page 490, a prospective MBA student wanted to predict starting salary upon graduation, based on program per-year tuition. The data are stored in FIMBA.
a. Construct a $95 \%$ confidence interval estimate of the mean starting salary upon graduation of an individual program with peryear tuition cost of $\$ 50,450$.
b. Construct a $95 \%$ prediction interval of the starting salary upon graduation of an individual program with per-year tuition cost of $\$ 50,450$.
c. Why is the interval in (a) narrower than the interval in (b)?

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01:49

Problem 61

In Problem 12.9 on page 490, an agent for a real estate company wanted to predict the monthly rent for one-bedroom apartments, based on the size of an apartment. The data are stored in RentSilverSpring.
a. Construct a $95 \%$ confidence interval estimate of the mean monthly rental for all one-bedroom apartments that are 800 square feet in size.
b. Construct a $95 \%$ prediction interval of the monthly rental for an individual one-bedroom apartment that is 800 square feet in size.
c. Explain the difference in the results in (a) and (b).

Nick Johnson
Nick Johnson
Numerade Educator
00:43

Problem 62

In Problem 12.8 on page 490, you used the internet users to predict Facebook users by countries. The data are stored in Internet.
a. Construct a $95 \%$ confidence interval estimate of the mean Facebook users for all countries that have 6.4 million internet users.
b. Construct a $95 \%$ prediction interval of Facebook users of an individual country that has 6.4 million internet users.
c. Explain the difference in the results in (a) and (b).

Madysn Cardinal
Madysn Cardinal
Numerade Educator
08:29

Problem 63

In Problem 12.10 on page 490, you used YouTube trailer views to predict movie weekend box office gross from data stored in Movie. A movie, about to be released, has 50 million YouTube trailer views.
a. What is the predicted weekend box office gross?
b. Which interval is more useful here, the confidence interval estimate of the mean or the prediction interval for an individual response? Explain.

Dela Akpalu
Dela Akpalu
Numerade Educator
02:26

Problem 64

What is the interpretation of the $Y$ intercept and the slope in the simple linear regression equation?

Ernest Castorena
Ernest Castorena
Numerade Educator
02:59

Problem 65

What is the interpretation of the coefficient of determination?

Jai Chadha
Jai Chadha
Numerade Educator
01:25

Problem 66

When is the unexplained variation (i.e., error sum of squares) equal to 0 ?

Carson Merrill
Carson Merrill
Numerade Educator
02:16

Problem 67

When is the explained variation (i.e., regression sum of squares) equal to 0 ?

Rashmi Sinha
Rashmi Sinha
Numerade Educator
04:02

Problem 68

Why should you always carry out a residual analysis as part of a regression model?

Carson Merrill
Carson Merrill
Numerade Educator
04:17

Problem 69

What are the assumptions of regression analysis?

Phumlani Ngcobo
Phumlani Ngcobo
Numerade Educator
01:43

Problem 70

How do you evaluate the assumptions of regression analysis?

Brandon Cleary
Brandon Cleary
Numerade Educator
05:11

Problem 71

When and how do you use the Durbin-Watson statistic?

Willis James
Willis James
Numerade Educator

Problem 72

What is the difference between a confidence interval estimate of the mean response, $\mu_{Y \mid X=X_i}$, and a prediction interval of $Y_{X=X_i}$ ?

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01:55

Problem 73

Can you use movie critics' opinions to forecast box office receipts on the opening weekend? The following data, stored in Tomatometer, indicate the Tomatometer rating, the percentage of professional critic reviews that are positive, and the receipts per theater (\$thousands) on the weekend a movie opened for ten movies:
$$
\begin{array}{lcc}
\text { Movie } & \text { Tomatometer Rating } & \text { Receipts } \\
\hline \text { The Mummy } & 16 & 7.8 \\
\text { Zookeeper's Wife } & 61 & 6.1 \\
\text { Beatriz at Dinner } & 80 & 28.4 \\
\text { The Hero } & 76 & 11.3 \\
\text { Wonder Woman } & 93 & 24.8 \\
\text { Baby Boss } & 52 & 13.3 \\
\text { The Circle } & 15 & 2.9 \\
\text { Dean } & 61 & 4.0 \\
\text { Baywatch } & 20 & 5.1 \\
\text { Churchill } & 38 & 1.9
\end{array}
$$
a. Use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of $b_0$ and $b_1$ in this problem.
c. Predict the mean receipts for a movie that has a Tomatometer rating of $55 \%$.
d. Should you use the model to predict the receipts for a movie that has a Tomatometer rating of $5 \%$ ? Why or why not?
e. Determine the coefficient of determination, $r^2$, and explain its meaning in this problem.
f. Perform a residual analysis. Is there any evidence of a pattern in the residuals? Explain.
g. At the 0.05 level of significance, is there evidence of a linear relationship between Tomatometer rating and receipts?
h. Construct a $95 \%$ confidence interval estimate of the mean receipts for a movie that has a Tomatometer rating of $55 \%$ and a $95 \%$ prediction interval of the receipts for a single movie that has a Tomatometer rating of $55 \%$.
i. Based on the results of (a)-(h), do you think that Tomatometer rating is a useful predictor of receipts on the first weekend a movie opens? What issues about these data might make you hesitant to use Tomatometer rating to predict receipts?

Dominador Tan
Dominador Tan
Numerade Educator

Problem 74

Management of a hypermarket chain in London has the business objective of developing a method to increase store sales and the number of customers who visit the store. To begin, management decided to develop a model to predict the weekly sales (in thousands of pounds) for individual stores in the chain, based on the number of customers who visited the store. A sample of 18 stores from the chain was selected. The data for weekly sales and number of customers for each of these 18 stores is stored in StoreSales&Customers.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients, $b_0$ and $b_1$ and state the regression equation.
b. Interpret the meaning of the slope in this equation.
c. Predict the mean sale for a store with 62,000 visitors.
d. Interpret the meaning of the coefficient of determination, $r^2$, in this problem.
e. Perform a residual analysis on the results and determine the adequacy of the model.
f. At the 0.05 level of significance, is there evidence of a significant relationship between weekly sales and the number of customers who visited the stores?
g. Construct a $95 \%$ confidence interval estimate of the population slope between the weekly sales and the number of customers who visited the stores.
h. What conclusions can you reach about the relationship between the number of customers who visited the store and its weekly sales?

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02:40

Problem 75

Measuring the height of a California redwood tree is very difficult because these trees grow to heights of over 300 feet. People familiar with these trees understand that the height of a California redwood tree is related to other characteristics of the tree, including the diameter of the tree at the breast height of a person. The data in Redwood represent the height (in feet) and diameter (in inches) at the breast height of a person for a sample of 21 California redwood trees.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$. State the regression equation that predicts the height of a tree based on the tree's diameter at breast height of a person.
b. Interpret the meaning of the slope in this equation.
c. Predict the mean height for a tree that has a breast height diameter of 25 inches.
d. Interpret the meaning of the coefficient of determination in this problem.
e. Perform a residual analysis on the results and determine the adequacy of the model.
f. Determine whether there is a significant relationship between the height of redwood trees and the breast height diameter at the 0.05 level of significance.
g. Construct a $95 \%$ confidence interval estimate of the population slope between the height of the redwood trees and breast height diameter.
h. What conclusions can you reach about the relationship of the diameter of the tree and its height?

Brandon Cleary
Brandon Cleary
Numerade Educator
01:42

Problem 76

You want to develop a model to predict the asking price of homes based on their size. A sample of 61 single-family houses listed for sale in Silver Spring, Maryland, a suburb of Washington, DC, is selected to study the relationship between asking price (in $$\$ $$thousands) and living space (in square feet), and the data is collected and stored in SilverSpring. (Hint: First determine which are the independent and dependent variables.)
a. Construct a scatter plot and, assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Use the prediction line developed in (a) to predict the mean asking price for a house whose living space is 2,000 square feet.
d. Determine the coefficient of determination, $r^2$, and interpret its meaning in this problem.
e. Perform a residual analysis on your results and evaluate the regression assumptions.
f. At the 0.05 level of significance, is there evidence of a linear relationship between asking price and living space?
g. Construct a $95 \%$ confidence interval estimate of the population slope.
h. What conclusions can you reach about the relationship between the living space and asking price?

Sheryl Ezze
Sheryl Ezze
Numerade Educator
01:42

Problem 77

You want to develop a model to predict the taxes of houses, based on asking price. A sample of 61 single-family houses listed for sale in Silver Spring, Maryland, a suburb of Washington, DC, is selected. The taxes (in $$\$ $$) and the asking price of the houses (in $$\$ $$ thousands) are recorded and stored in SilverSpring . (Hint: First determine which are the independent and dependent variables.)
a. Construct a scatter plot and, assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Use the prediction line developed in (a) to predict the mean taxes for a house whose asking price is $$\$ 400,000$$.
d. Determine the coefficient of determination, $r^2$, and interpret its meaning in this problem.
e. Perform a residual analysis on your results and evaluate the regression assumptions.
f. At the 0.05 level of significance, is there evidence of a linear relationship between taxes and asking price?
g. What conclusions can you reach concerning the relationship between taxes and asking price?

Sheryl Ezze
Sheryl Ezze
Numerade Educator
01:09

Problem 78

An analyst has the objective of predicting the return on average tangible common equity (ROATCE) of banks. The analyst begins by using efficiency ratio, a measure of a bank's ability to turn resources into revenue. A sample of 100 American banks is selected and stored in AmericanBanks.
a. Construct a scatter plot and, assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Use the prediction line developed in (a) to predict the mean ROATCE for a bank with an efficiency ratio of $60 \%$.
d. Determine the coefficient of determination, $r^2$, and interpret its meaning in this problem.
e. Perform a residual analysis on your results and evaluate the regression assumptions.
f. At the 0.05 level of significance, is there evidence of a linear relationship between efficiency ratio and ROATCE?
g. Construct a $95 \%$ confidence interval estimate of the mean ROATCE of banks with an efficiency ratio of $60 \%$ and a $95 \%$ prediction interval of the ROATCE for a particular bank with an efficiency ratio of $60 \%$.
h. Construct a $95 \%$ confidence interval estimate of the population slope.
i. What conclusions can you reach concerning the relationship between efficiency ratio and ROATCE?

Lucas Finney
Lucas Finney
Numerade Educator
01:05

Problem 79

An accountant for a large department store has the business objective of developing a model to predict the amount of time it takes to process invoices. Data are collected from the past 32 working days, and the number of invoices processed and completion time (in hours) are stored in Invoice. (Hint: First determine which are the independent and dependent variables.)
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Use the prediction line developed in (a) to predict the mean amount of time it would take to process 150 invoices.
d. Determine the coefficient of determination, $r^2$, and interpret its meaning.
e. Plot the residuals against the number of invoices processed and also against time.
f. Based on the plots in (e), does the model seem appropriate?
g. Based on the results in (e) and (f), what conclusions can you reach about the validity of the prediction made in (c)?
h. What conclusions can you reach about the relationship between the number of invoices and the completion time?

Dominador Tan
Dominador Tan
Numerade Educator
01:33

Problem 80

On January 28,1986 , the space shuttle Challenger exploded, and seven astronauts were killed. Prior to the launch, the predicted atmospheric temperature was for freezing weather at the launch site. Engineers for the manufacturer of the rocket motor prepared charts to make the case that the launch should not take place due to the cold weather. These arguments were rejected, and the launch tragically took place. Upon investigation after the tragedy, experts agreed that the disaster occurred because of leaky rubber O-rings that did not seal properly due to the cold temperature. Data indicating the atmospheric temperature at the time of 23 previous launches and the O-ring damage index are stored in O-Ring . (Data from flight 4 is omitted due to unknown O-ring condition.)
a. Construct a scatter plot for the seven flights in which there was $\mathrm{O}$-ring damage ( $\mathrm{O}$-ring damage index $\neq 0$ ). What conclusions, if any, can you reach about the relationship between atmospheric temperature and O-ring damage?
b. Construct a scatter plot for all 23 flights.
c. Explain any differences in the interpretation of the relationship between atmospheric temperature and O-ring damage in (a) and (b).
d. Based on the scatter plot in (b), provide reasons why a prediction should not be made for an atmospheric temperature of $31^{\circ} \mathrm{F}$, the temperature on the morning of the launch of the Challenger.
e. Although the assumption of a linear relationship may not be valid for the set of $23 \mathrm{flights}$, fit a simple linear regression model to predict $\mathrm{O}$-ring damage, based on atmospheric temperature.
f. Include the prediction line found in (e) on the scatter plot developed in (b).
g. Based on the results in (f), do you think a linear model is appropriate for these data? Explain.
h. Perform a residual analysis. What conclusions do you reach?

Harmender Singh Yadav
Harmender Singh Yadav
Numerade Educator
01:55

Problem 81

A baseball analyst would like to study various team statistics for a recent season to determine which variables might be useful in predicting the number of wins achieved by teams during the season. He begins by using a team's earned run average (ERA), a measure of pitching performance, to predict the number of wins. He collects the team ERA and team wins for each of the 30 Major League Baseball teams and stores these data in Baseball. (Hint: First determine which are the independent and dependent variables.)
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Use the prediction line developed in (a) to predict the mean number of wins for a team with an ERA of 4.50.
d. Compute the coefficient of determination, $r^2$, and interpret its meaning.
e. Perform a residual analysis on your results and determine the adequacy of the fit of the model.
f. At the 0.05 level of significance, is there evidence of a linear relationship between the number of wins and the ERA?
g. Construct a $95 \%$ confidence interval estimate of the mean number of wins expected for teams with an ERA of 4.50.
h. Construct a $95 \%$ prediction interval of the number of wins for an individual team that has an ERA of 4.50 .
i. Construct a $95 \%$ confidence interval estimate of the population slope.
j. The 30 teams constitute a population. In order to use statistical inference, as in (f) through (i), the data must be assumed to represent a random sample. What "population" would this sample be drawing conclusions about?
k. What other independent variables might you consider for inclusion in the model?
1. What conclusions can you reach concerning the relationship between ERA and wins?

Dominador Tan
Dominador Tan
Numerade Educator
09:25

Problem 82

Can you use the annual revenues generated by National Basketball Association (NBA) franchises to predict franchise values? Figure 2.17 on page 102 shows a scatter plot of revenue with franchise value, and Figure 3.13 on page 189, shows the correlation coefficient. Now, you want to develop a simple linear regression model to predict franchise values based on revenues. (Franchise values and revenues are stored in NBAValues .)
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Predict the mean value of an NBA franchise that generates $$\$ 150$$ million of annual revenue.
d. Compute the coefficient of determination, $r^2$, and interpret its meaning.
e. Perform a residual analysis on your results and evaluate the regression assumptions.
f. At the 0.05 level of significance, is there evidence of a linear relationship between the annual revenues generated and the value of an NBA franchise?
g. Construct a $95 \%$ confidence interval estimate of the mean value of all NBA franchises that generate $$\$ 150$$ million of annual revenue.
h. Construct a $95 \%$ prediction interval of the value of an individual NBA franchise that generates $$\$ 150$$ million of annual revenue.
i. Compare the results of (a) through (h) to those of the European soccer teams in Problem 12.83.

Sneha Ravi
Sneha Ravi
Numerade Educator
01:17

Problem 83

In Problem 12.82 you used annual revenue to develop a model to predict the franchise value of National Basketball Association (NBA) teams. Can you also use the annual revenues generated by European soccer teams to predict franchise values? (European soccer team values and revenues are stored in SoccerValues.)
a. Repeat Problem 12.82 (a) through (h) for the European soccer teams.
b. Compare the results of (a) to those of the NBA franchises in Problem 12.82.

Carson Merrill
Carson Merrill
Numerade Educator
20:22

Problem 84

A real estate broker in Dubai has to develop a model to predict the price of a house based on the number of its rooms. She collected data about the price of 50 houses and the number of rooms in each of the 50 houses. The data are stored in HousePricesRooms.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients $b_0$ and $b_1$.
b. Interpret the meaning of the $Y$ intercept, $b_0$, and the slope, $b_1$, in this problem.
c. Predict the mean price of a house that has three rooms.
d. Compute the coefficient of determination, $r^2$, and interpret its meaning.
e. Perform a residual analysis on your results and evaluate the regression assumptions.
f. At the 0.05 level of significance, is there evidence of a linear relationship between the price of a house and the number of rooms it has?
g. Construct a $95 \%$ confidence interval estimate of the mean price of a house that has three rooms.
h. Construct a $95 \%$ prediction interval of the price of a house with three rooms.

Jon Southam
Jon Southam
Numerade Educator
02:46

Problem 85

In Problems 12.8, 12.20, 12.30, 12.46, 12.62, 12.82, and 12.83, you developed regression models to predict value of the internet, Facebook, and soccer teams. Now, write a report based on the models you developed. Append to your report all appropriate charts and statistical information.

James Kiss
James Kiss
Numerade Educator