4 Management proposed the following regression model to predict sales at a fast-food outlet.y = Bo + B121 + B2 + Bas +ewhere*1 = number of competitors within one mileE2 = population within one mile (1000s)S1 if drive-up window presentotherwisey = sales ($1000s)The following estimated regression equation was developed after 20 outlets were surveyed.ý = 10.1- 4.2 + 6.8z2 +15.3r3a. 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 8,000 within one mile, and no drive-up window.Estimate of sales = $c. Predict sales for a store with one competitor, a population of 3,000 within 1 mile, and a drive-up window.Estimate of sales = $

Introduction: The estimated regression equation to predict sales ($1000s), y of a fast-food outlet…

Management proposed the following regression model to predict sales at a fast-food outlet. y = Bo + Biz + B2e + Bzs +e where 21 = number of competitors within one mile 22 = population within one mile (1000s) Si if drive-up window present 10 otherwise y= sales ($1000s) The following estimated regression equation was developed after 20 outlets were surveyed. ý = 10.1 - 4.2z + 6.8z2 + 15.3z 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 8,000 within one mile, and no drive-up window. Estimate of sales = $ c. Predict sales for a store with one competitor, a population of 3,000 within 1 mile, and a drive-up window. Estimate of sales =$

Management proposed the following regression model to predict sales at a fast-food outlet. y = Bo + Biz + B2e + Bzs +e where 21 = number of competitors within one mile 22 = population within one mile (1000s) Si if drive-up window present 10 otherwise y= sales ($1000s) The following estimated regression equation was developed after 20 outlets were surveyed. ý = 10.1 - 4.2z + 6.8z2 + 15.3z 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 8,000 within one mile, and no drive-up window. Estimate of sales = $ c. Predict sales for a store with one competitor, a population of 3,000 within 1 mile, and a drive-up window. Estimate of sales =$

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...

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Life 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?2300
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?
3.4 SKILL BUILDING EXERCISES Getting Regression Lines Only Find the equation of the regression line for the following data set. x 1 2 3 y 3 3 4
Demand 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 2.00