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…

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Does Table 2 represent a linear function? If so, finda linear equation that models the data.
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?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________
If you are performing a break-even analysis for a business and their cost and revenue equations are dependent, explain what this means for the company's profit margins.
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 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 Ś
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?
William 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). His estimated regression equation was Crime = 428 + 0.050 Income. We can conclude that Multiple Choice crime causes additional income in each city. the intercept is irrelevant because zero median income makes no sense. wealthy individuals tend to commit most crimes. there is no significant relationship between income and crime.
Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price and x = house size (sq ft) for houses in a large city. The true regression line is y = 23,000 + 48x and ? = 4000. (a) What is the average change in price associated with one extra sq ft of space?$ With an additional 100 sq ft of space?$ (b) What proportion of 1800 sq ft homes would be priced over $110,000? (Round your answer to four decimal places.)Under $100,000? (Round your answer to four decimal places.)
Suppose that a researcher studying the weight of female college athletes wants to predict the weights based on height, measured in inches, and the percentage of body fat of an athlete. The researcher calculates the regression equation as (weight) = 3.544*(height) + 1.064*(percent body fat) - 87.076. If a female athlete is 64 inches tall and has a 21 percentage of body fat, what is her expected weight? Question 24 options: 1) 55.444 2) We do not know the observations in the data set, so we cannot answer that question. 3) 162.084 4) 249.16 5) 336.236

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