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1. Data were collected on sales of mountain bikes in 30 sporting goods stores. The regression model was y = total sales (thousands of dollars), x₁ = display floor space (square meters), x2 = competitors' advertising expenditures (thousands of dollars), and x3 = advertised price (dollars per unit). A summary of the regression output is below. Variable (nickname) Intercept Floor Space Competing Ads Price Coefficient 1225.44 11.52 -6.935 -0.1496 (a) Write the fitted regression equation. Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. (b-1) Put an X in the correct answer circle. The coefficient of Floor Space says that each additional square foot of floor space... O adds about 11.52 to sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-2) Put an X in the correct answer circle. The coefficient of Competing Ads says that each additional $1,000 of "competitors' advertising expenditures"... reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-3) Put an X in the correct answer circle. The coefficient of Price says that each additional $1 of advertised price... O reduces sales by about 0.1496 from sales (in thousands of dollars). O reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). (c) The intercept is meaningful. O True (d) Make a prediction for Sales when FloorSpace= 80, Competing Ads = 100, and Price = 1,200. False

1. Data were collected on sales of mountain bikes in 30 sporting goods stores. The regression model was y = total sales (thousands of dollars), x₁ = display floor space (square meters), x2 = competitors' advertising expenditures (thousands of dollars), and x3 = advertised price (dollars per unit). A summary of the regression output is below. Variable (nickname) Intercept Floor Space Competing Ads Price Coefficient 1225.44 11.52 -6.935 -0.1496 (a) Write the fitted regression equation. Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. (b-1) Put an X in the correct answer circle. The coefficient of Floor Space says that each additional square foot of floor space... O adds about 11.52 to sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-2) Put an X in the correct answer circle. The coefficient of Competing Ads says that each additional $1,000 of "competitors' advertising expenditures"... reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-3) Put an X in the correct answer circle. The coefficient of Price says that each additional $1 of advertised price... O reduces sales by about 0.1496 from sales (in thousands of dollars). O reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). (c) The intercept is meaningful. O True (d) Make a prediction for Sales when FloorSpace= 80, Competing Ads = 100, and Price = 1,200. False

MATLAB: An Introduction with Applications
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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4. Consider the following data set on the top 20 small cruise ships in the world as of 2012. The 4 primary variables are ratings (scores) from passenger surveys. A score of 100 is a perfect score. The ships were rated on the quality of their on-ship activities (Itineraries), off-ship activities (Excursions), and food. They were also given an overall rating. Ship Seabourn Odyssey Seabourn Pride National Geographic Endeavor Seabourn Sojourn Paul Gauguin Seabourn Legend Seabourn Spirit Silver Explorer Silver Spirit Seven Seas Navigator Silver Whisperer National Geographic Explorer Silver Cloud Celebrity Xpedition Silver Shadow Silver Wind SeaDream II Wind Star Wind Surf Wind Spirit Overall 94.4 93.0 92.9 91.3 90.5 90.3 90.2 89.9 89.4 89.2 89.2 89.1 88.7 87.2 87.2 86.6 86.2 86.1 86.1 85.2 Itineraries 94.6 96.7 100.0 88.6 95.1 92.5 96.0 92.6 94.7 90.6 90.9 93.1 92.6 93.1 91.0 94.4 95.5 94.9 92.1 93.5 4 Excursions 90.9 84.2 100.0 94.8 87.9 82.1 86.3 92.6 85.9 83.3 82.0 93. 78.3 91.7 75.0 78.1 77.4 76.5 72.3 77.4 Food 97.8 96.7 88.5 97.1 91.2 98.8 92.0 88.9 90.8 90.5 88.6 89. 91.3 73.6 89.7 91.6 90.9 91.5 89.3 91.9 (a) Which ship has the best overall rating? (b) Run a regression with y = overall satisfaction rating and the other three variables as the variables. Use the results to answer the following. (b-1) Which variable has the strongest impact on Overall Satisfaction? (b-2) One or more of these variables negatively affects Overall Satisfaction. (b-3) This regression passes a residual analysis. O True (b-4) Predict the rating of a ship with Itinerary Score Excursion Score = Food Score = 90. (b-5) What business decision(s) would you make if you were in charge of one of these cruise ships after seeing these regression results? True O False O False
Transcribed Image Text:4. Consider the following data set on the top 20 small cruise ships in the world as of 2012. The 4 primary variables are ratings (scores) from passenger surveys. A score of 100 is a perfect score. The ships were rated on the quality of their on-ship activities (Itineraries), off-ship activities (Excursions), and food. They were also given an overall rating. Ship Seabourn Odyssey Seabourn Pride National Geographic Endeavor Seabourn Sojourn Paul Gauguin Seabourn Legend Seabourn Spirit Silver Explorer Silver Spirit Seven Seas Navigator Silver Whisperer National Geographic Explorer Silver Cloud Celebrity Xpedition Silver Shadow Silver Wind SeaDream II Wind Star Wind Surf Wind Spirit Overall 94.4 93.0 92.9 91.3 90.5 90.3 90.2 89.9 89.4 89.2 89.2 89.1 88.7 87.2 87.2 86.6 86.2 86.1 86.1 85.2 Itineraries 94.6 96.7 100.0 88.6 95.1 92.5 96.0 92.6 94.7 90.6 90.9 93.1 92.6 93.1 91.0 94.4 95.5 94.9 92.1 93.5 4 Excursions 90.9 84.2 100.0 94.8 87.9 82.1 86.3 92.6 85.9 83.3 82.0 93. 78.3 91.7 75.0 78.1 77.4 76.5 72.3 77.4 Food 97.8 96.7 88.5 97.1 91.2 98.8 92.0 88.9 90.8 90.5 88.6 89. 91.3 73.6 89.7 91.6 90.9 91.5 89.3 91.9 (a) Which ship has the best overall rating? (b) Run a regression with y = overall satisfaction rating and the other three variables as the variables. Use the results to answer the following. (b-1) Which variable has the strongest impact on Overall Satisfaction? (b-2) One or more of these variables negatively affects Overall Satisfaction. (b-3) This regression passes a residual analysis. O True (b-4) Predict the rating of a ship with Itinerary Score Excursion Score = Food Score = 90. (b-5) What business decision(s) would you make if you were in charge of one of these cruise ships after seeing these regression results? True O False O False
1. Data were collected on sales of mountain bikes in 30 sporting goods stores. The regression model was y = total sales (thousands of dollars), *₁ = display floor space (square meters), 2 = competitors' advertising expenditures (thousands of dollars), and x3 = advertised price (dollars per unit). A summary of the regression output is below. Variable (nickname) Intercept FloorSpace Competing Ads Price Coefficient 1225.44 11.52 -6.935 -0.1496 (a) Write the fitted regression equation. Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. (b-1) Put an X in the correct answer circle. The coefficient of FloorSpace says that each additional square foot of floor space... O adds about 11.52 to sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). takes away 0.1496 from sales (in thousands of dollars). (b-2) Put an X in the correct answer circle. The coefficient of CompetingAds says that each additional $1,000 of "competitors' advertising expenditures"... O reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-3) Put an X in the correct answer circle. The coefficient of Price says that each additional $1 of advertised price... reduces sales by about 0.1496 from sales (in thousands of dollars). O reduces sales by about 6.935 from sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). (c) The intercept is meaningful. (d) Make a prediction for Sales when FloorSpace = 80, CompetingAds = 100, and Price = 1,200. True False
Transcribed Image Text:1. Data were collected on sales of mountain bikes in 30 sporting goods stores. The regression model was y = total sales (thousands of dollars), *₁ = display floor space (square meters), 2 = competitors' advertising expenditures (thousands of dollars), and x3 = advertised price (dollars per unit). A summary of the regression output is below. Variable (nickname) Intercept FloorSpace Competing Ads Price Coefficient 1225.44 11.52 -6.935 -0.1496 (a) Write the fitted regression equation. Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and other values to 2 decimal places. (b-1) Put an X in the correct answer circle. The coefficient of FloorSpace says that each additional square foot of floor space... O adds about 11.52 to sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). takes away 0.1496 from sales (in thousands of dollars). (b-2) Put an X in the correct answer circle. The coefficient of CompetingAds says that each additional $1,000 of "competitors' advertising expenditures"... O reduces sales by about 6.935 from sales (in thousands of dollars). O takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). O takes away 0.1496 from sales (in thousands of dollars). (b-3) Put an X in the correct answer circle. The coefficient of Price says that each additional $1 of advertised price... reduces sales by about 0.1496 from sales (in thousands of dollars). O reduces sales by about 6.935 from sales (in thousands of dollars). takes away 11.52 from sales (in thousands of dollars). O adds about 6.935 to sales (in thousands of dollars). (c) The intercept is meaningful. (d) Make a prediction for Sales when FloorSpace = 80, CompetingAds = 100, and Price = 1,200. True False
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