M3 The scatter plot and linear regression models for the yearly revenue of restaurants (R) and supermarkets (S) are given in the popup below. The revenue forrestaurants can be modeled by the equation R 11.5n+180.0, and for supermarkets can be modeled by the equation S-5.5n+225.5, where n is the number ofyears since 1986 and revenue is in billions of dollarsa. Interpret the slope of each modelb. Use the model to predict the revenue of restaurants and the revenue of supermarkets in 2017c. Estimate the first year that restaurant revenue will exceed supermarket revenue by at least 150 billion dollars.Click the icon to view the scatter plot and data table of yearly revenueCOREDa. Interpret the slope of each modelThe slope of the linear regression model for restaurants shows that the revenue isat a rate of billion dollars per year.(Type an integer or a decimal)

Given, Restaurant regression model, R = 11.5n + 180 Supermarket regression model, S = 5.5n + 225.5…

revenue is in billions of dollars 1986 at the slope of each model e model to pres enue of restaurants and the revenue of supermarkets in 2017 te the first year that restaurant revenue will exceed supermarket revenue by at least 150 billion dollars the icon to view the scatter plot and data table of yearly revenue met the slope of each model of the linear regression model for restaurants shows that the revenue is at a rate of billion dollars per year.

revenue is in billions of dollars 1986 at the slope of each model e model to pres enue of restaurants and the revenue of supermarkets in 2017 te the first year that restaurant revenue will exceed supermarket revenue by at least 150 billion dollars the icon to view the scatter plot and data table of yearly revenue met the slope of each model of the linear regression model for restaurants shows that the revenue is at a rate of billion dollars per year.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

See similar textbooks

Similar questions

The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?
As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…
A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.
Given the following summary statistics, determine the regression equation used to predict y from Ta Round all answers to 2 decimal places. slope - y-intercept Sy SI T 15 Y 1.02 1.6 -0.71 20.65 77-9 Use the exact value of slope when calculating the y-intercept.
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is GPA). Estimated College GPA = 2.56 + 0.1582 High School GPA GPAs College GPA High School GPA 3.96 4.42 2.81 3.91 3.53 4.21 3.27 2.76 3.58 4.95 2.07 4.24 Copy Data Step 2 of 3: Compute the mean square error (s2) for the model. Round your answer to four decimal places.
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years. Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? Best Actress 27 30 30 61 30 32 46 28 61 22 43 56 D Best Actor 42 39 38 45 51 49 59 51 38 57 45 34 Find the equation of the regression line. y = + (Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.) The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years is years old. (Round to the nearest whole number as needed.) Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? the predicted age is the actual winner's age.
GENERAL LINEAR MODEL I 1. Consider the simple regression model y; = B2x; + ɛ Find the least squares estimator b, and show E §, &, = 0
How to run a linear regression analysis on excel based on the following data: Fatalities Ln Safety Belt Rate 1071 3.951243719 1138 4.058717385 996 4.257030144 991 4.374498368 1038 4.365643155 1004 4.348986781 1154 4.382026635 1148 4.404277244 1207 4.417635062 1110 4.410371108 969 4.455509411 848 4.49980967 862 4.515245478 895 4.477336814 865 4.494238625 853 4.577798989 820 4.561218298 850 4.535820108 1083 4.521788577 948 4.531523646 953 4.519612298 856 4.525044142
The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y = - 0.33x+95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 Year 1960 07 -> 1970 1980 91 60 60 Years after 1060 88 Years after 1980 1990 84 OC. OD. 2000 83 100 , 100 0- 04 60 60 Years after 1960 Years after 1960 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is -0.33% per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about 78.5 %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year…
Suppose that we obtained an estimated equation for the regression of weekly sales of palm pilots and the price charged during the week. Interpret the constants b0 for the product brand manager.
Stores commonly offer a cheaper unit price for large quantity purchases. Quantity 1 2 5 10 20 Unit Price $100.00 $80.00 $70.00 $50.00 $40.00 a. Use regression to find a logarithmic equation to model the data. Round the numbers in your equation to 2 decimal places. y = a + bln(z) with You b b. Use your equation to find an appropriate unit price for a customer who purchases 15 items. c. Use your equation to find an appropriate unit price for a customer who purchases 25 items. $
Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer