The following estimated regression equation has been proposed to predict sales at a fast food outlet.ŷ = 16 - 4x₁ + 8x₂ + 14x3wherefl1X1= the number of competitors within 1 milethe population within 1 mile (in 1,000s)X3= 1 if drive-up windows are present; 0 otherwiseŷ = estimated sales (in $1,000s)X2(a) What is the interpretation of 14 (the coefficient of x3) in the regression equation? (Give the answer in dollars.)Sales in dollars at outlets withdrive-up windows are on average $ 14?www.comm(b) Predict sales for an outlet with 3 competitors within one mile, a population of 14,000 within one mile, and one drive-up window (give the answer in dollars).$130Need Help? Read It(c) Predict sales for an outlet with 3 competitors within one mile, a population of 14,000 within one mile, and no drive-up window (give the answer in dollars).$112004f2@2f3#*3f41/7HT$4f540doO5f64-^6f7♫+C&f87IAAXhigher than those withoutⒸ*f9Dhp8OKINf10EFINOf11drive-up windows.21f12.insertprt sc+

Given The equation is as follows: y^=16-4x1+8x2+14x3wherex1=the number of competitors within 1…

Answered: The following estimated regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table gives the amount of money (in dollars) spent on football by a major university. Letting xx represent the number of years since 2008, and letting yy represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2008 2009 2010 2011 2012 2013 Dollars spent on football 169,000 197,000 210,000 222,000 245,000 285,000 The equation of the line of best fit is: Hint Predict the amount of money that will be spent on football in the year 2026. $ Predict the amount of money that will be spent on football in the year 2041. $
Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1) (cost of delivery) = 1.468*(weight) - 23.015 2) (weight) = -23.015*(cost of delivery) + 1.468 3) (cost of delivery) = -23.015*(weight) + 1.468 4) (cost of delivery) = 1.468*(weight) 5) (weight) = 1.468*(cost of delivery) - 23.015
Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.
A marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…
The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y = 1.493x - 83.403. Complete parts (a) and (b) below. A Average Weekly Wages (state), x Average Weekly Wages (federal), y 764 1003 766 1048 791 1119 (a) Find the coefficient of determination and interpret the result. r² = 0 (Round to three decimal places as needed.) 800 1152 843 1201 887 1250 924 1277 939 1306
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The accompanying data table gives the value and the annual revenue for 15 major sport teams. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated.
There is a linear relationship between the number of chirps made by the stiped ground cricket and the air temperature. It was determined that the linear regression model is: y = 25.2 + 3.3x where x is the number of chirps per minute and y is the estimated temperature in degrees Fahrenheit. What is the temperature if a cricket chirps 18 times? Round to the nearest degree. Di not include units.
For 39 nations, a correlation of 0.887 was found between y = Internet use (%) and x = gross domestic product (GDP, in thousands of dollars per capita). The regression equation is y = -3.68 + 1.73x. Complete parts (a) through (c). a. Based on the correlation value, the slope had to be positive. Why? A. The slope and correlation are positive because gross domestic product could not be negative. B. The correlation and the slope are positive because the y-intercept is negative. C. That is a very unusual fact, because the slope and correlation usually have different signs. D. Although slope and correlation usually have different values, they always have the same sign.
In 2012, the CDC published data on the average weight of American female children by year. Using ages 1−7 of this data, researchers made a linear regression with age in years as the x variable and weight in pounds as the y variable. The equation of the line of best fit is yˆ=5.78x+17.56. According to the line of best fit, what would you predict the weight would be for a 14-year-old female? Is it reasonable to use this line of best fit to make this prediction?

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