CENGAGENOW FOR ANDERSON/SWEENEY/WILLIAM
13th Edition
ISBN: 9781337094399
Author: Cochran
Publisher: IACCENGAGE
expand_more
expand_more
format_list_bulleted
Concept explainers
Textbook Question
Chapter 15.7, Problem 34E
Management proposed the following regression model to predict sales at a fast-food outlet.
y = β0 + β1x1 + β2x2 + β3x3 + ε
where
x1 = number of competitors within one mile
x2 = population within one mile (1000s)
y = sales ($1000s)
The following estimated regression equation was developed after 20 outlets were surveyed.
ŷ = 10.1 − 4.2x1 + 6.8x2 + 15.3x3
- a. What is the expected amount of sales attributable to the drive-up window?
- b. Predict sales for a store with two competitors, a population of 8000 within one mile, and no drive-up window.
- c. Predict sales for a store with one competitor, a population of 3000 within one mile, and a drive-up window.
Expert Solution & Answer
Trending nowThis is a popular solution!
Students have asked these similar questions
A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently
sold homes in the space, the following variables are used in a multiple regression model.
y = sales price (in thousands of dollars)
x₁ =
total floor area (in square feet)
x₂ =
number of bedrooms
x3 distance to nearest high school (in miles)
=
The estimated model is as follows.
=76+0.098x₁ +16x₂ - 8x3
Answer the questions below for the interpretation of the coefficient of X₂ in this model.
(a) Holding the other variables fixed, what is the average change in sales price for each
additional bedroom in a house?
dollars
(b) Is this change an increase or a decrease?
O increase
O decrease
X
A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three
ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.
Click the icon to view the table of summated ratings and cost per person.
.....
a. Construct a scatter plot. Choose the correct graph below.
A.
Ов.
С.
D.
ACost ($)
90-
ACost ($)
90-
ACost ($)
90-
ACost ($)
90-
0-
0-
90
90
90
90
Rating
Rating
Rating
Rating
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,.
bo = and b,
(Round to two decimal places as needed.)
c. Interpret the meaning of the Y-intercept, bo, and the slope, b,. Choose the correct answer below.
O A.…
A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the
accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.
Click the icon to view the table of summated ratings and cost per person.
a. Construct a scatter plot. Choose the correct graph below.
O A.
Ов.
OC.
OD.
ACost ($)
90-
Q
A Cost ($)
904
A Cost ($)
90-
ACost ($)
90-
0-
0-
0-
0-
90
Rating
90
Rating
90
90
Rating
Rating
Summated ratings and cost per
person
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b,.
bo =D and b, =O
(Round to two decimal places as needed.)
Summated Rating Cost ($ per person)|9
c. Interpret the…
Chapter 15 Solutions
CENGAGENOW FOR ANDERSON/SWEENEY/WILLIAM
Ch. 15.2 - The estimated regression equation for a model...Ch. 15.2 - Consider the following data for a dependent...Ch. 15.2 - In a regression analysis involving 30...Ch. 15.2 - A shoe store developed the following estimated...Ch. 15.2 - The owner of Showtime Movie Theaters, Inc., would...Ch. 15.2 - The National Football League (NFL) records a...Ch. 15.2 - PC Magazine provided ratings for several...Ch. 15.2 - The Cond Nast Traveler Gold List provides ratings...Ch. 15.2 - The Professional Golfers Association (PGA)...Ch. 15.2 - Prob. 10E
Ch. 15.3 - In exercise 1, the following estimated regression...Ch. 15.3 - Prob. 12ECh. 15.3 - In exercise 3, the following estimated regression...Ch. 15.3 - In exercise 4, the following estimated regression...Ch. 15.3 - In exercise 5, the owner of Showtime Movie...Ch. 15.3 - In exercise 6, data were given on the average...Ch. 15.3 - Prob. 17ECh. 15.3 - Refer to exercise 10, where Major League Baseball...Ch. 15.5 - In exercise 1, the following estimated regression...Ch. 15.5 - Refer to the data presented in exercise 2. The...Ch. 15.5 - The following estimated regression equation was...Ch. 15.5 - In exercise 4, the following estimated regression...Ch. 15.5 - Prob. 23ECh. 15.5 - Prob. 24ECh. 15.5 - The Cond Nast Traveler Gold List for 2012 provided...Ch. 15.5 - In exercise 10, data showing the values of several...Ch. 15.6 - In exercise 1, the following estimated regression...Ch. 15.6 - Refer to the data in exercise 2. The estimated...Ch. 15.6 - In exercise 5, the owner of Showtime Movie...Ch. 15.6 - In exercise 24, an estimated regression equation...Ch. 15.6 - The American Association of Individual Investors...Ch. 15.7 - Consider a regression study involving a dependent...Ch. 15.7 - Consider a regression study involving a dependent...Ch. 15.7 - Management proposed the following regression model...Ch. 15.7 - Refer to the Johnson Filtration problem introduced...Ch. 15.7 - This problem is an extension of the situation...Ch. 15.7 - The Consumer Reports Restaurant Customer...Ch. 15.7 - A 10-year study conducted by the American Heart...Ch. 15.8 - Data for two variables, x and y, follow. xi 1 2 3...Ch. 15.8 - Data for two variables, x and y, follow. xi 22 24...Ch. 15.8 - Exercise 5 gave the following data on weekly gross...Ch. 15.8 - The following data show the curb weight,...Ch. 15.8 - The Ladies Professional Golfers Association (LPGA)...Ch. 15.9 - Refer to the Simmons Stores example introduced in...Ch. 15.9 - In Table 15.12 we provided estimates of the...Ch. 15.9 - Community Bank would like to increase the number...Ch. 15.9 - Over the past few years the percentage of students...Ch. 15.9 - The Tire Rack maintains an independent consumer...Ch. 15 - The admissions officer for Clearwater College...Ch. 15 - The personnel director for Electronics Associates...Ch. 15 - A partial computer output from a regression...Ch. 15 - Recall that in exercise 49, the admissions officer...Ch. 15 - Recall that in exercise 50 the personnel director...Ch. 15 - The Tire Rack, Americas leading online distributor...Ch. 15 - The Department of Energy and the U.S....Ch. 15 - A portion of a data set containing information for...Ch. 15 - Fortune magazine publishes an annual list of the...Ch. 15 - Consumer Research, Inc., is an independent agency...Ch. 15 - Matt Kenseth won the 2012 Daytona 500, the most...Ch. 15 - Finding the Best Car Value When trying to decide...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forward
- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardXYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?arrow_forwardDemand for Candy Bars In this problem you will determine a linear demand equation that describes the demand for candy bars in your class. Survey your classmates to determine what price they would be willing to pay for a candy bar. Your survey form might look like the sample to the left. a Make a table of the number of respondents who answered yes at each price level. b Make a scatter plot of your data. c Find and graph the regression line y=mp+b, which gives the number of respondents y who would buy a candy bar if the price were p cents. This is the demand equation. Why is the slope m negative? d What is the p-intercept of the demand equation? What does this intercept tell you about pricing candy bars? Would you buy a candy bar from the vending machine in the hallway if the price is as indicated. Price Yes or No 50 75 1.00 1.25 1.50 1.75 2.00arrow_forward
- A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below. Click the icon to view the table of summated ratings and cost per person. a. Construct a scatter plot. Choose the correct graph below. O A. 90+ 0 0 Cost ($) The M Rating 90 Q O B. A Cost (5) 90+ 0 H +4 Alpe Rating 90 Q b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b₁. bo= and b₁ = (Round to two decimal places as needed.) C O C. 90+ 0- Cost (S) HA Rating 90 Q Summated Ratings and Cost Per Person Summated Rating Cost ($ per person) 40 48 60 61 42 40 43 55 67 69…arrow_forwardA real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y sales price (in thousands of dollars) x₁ = total floor area (in square feet) X₂= number of bedrooms x3² distance to nearest high school (in miles) The estimated model is as follows. y=121 +0.083x₁ +23x₂ - 4x3 Answer the questions below for the interpretation of the coefficient of x₁ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each 100-square-foot increase in floor area? dollars (b) Is this change an increase or a decrease? increase decrease X Śarrow_forwardA linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lrarrow_forward
- A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation F Next page JUN 2 12 étv T Ps Lrarrow_forwardThe U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. What proportion of variation in the number of complaints can be explained by hourly wages? From the results shown above, write the regression equation If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?arrow_forwardA marketing analysit is studying the relashionship between X = money spent on television advertising and Y = increase in slae. A simple linear regression model relates x and y as follows Y = 27.5 + 1.19 X What is the average change in sales associated with an additional 1 dollor spent on advertising? Group of answer choices A. For every additional 1 dollor spent on advertising, sales decreases by 1.19 dollars. B. For every additional 1 dollor spent on advertising, sales increase by 1.19 dollars. C. For every additional 1 dollor spent on advertising, sales increase by 28.69 dollars. D. For every additional 1 dollor spent on advertising, sales decreases by 28.69 dollars. E. For every additional 1 dollor spent on advertising, sales increase by 27.5 dollars.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage LearningGlencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
- College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningAlgebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Cengage Learning
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Algebra and Trigonometry (MindTap Course List)
Algebra
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY