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3. Travelers who have no intention of showing up often fall to шег travelers are known, in the parlance of the hospitality trade, as "no-shows". To protect against no-shows and late cancellations, hotels invariably overbook rooms. A recent study examined the problems of over-booking rooms in the hotel industry. The following data, extracted from the study represent the daily numbers of late cancellations and no-shows for a random sample of 10 days at a hotel: 18, 16, 16, 16, 14, 18, 16, 18, 14, 19. c) Count the number of measurements that actually fall within the interval(-28, +2s) and express the interval count as a percentage of total number of measurements. at least 75% 100%

3. Travelers who have no intention of showing up often fall to шег travelers are known, in the parlance of the hospitality trade, as "no-shows". To protect against no-shows and late cancellations, hotels invariably overbook rooms. A recent study examined the problems of over-booking rooms in the hotel industry. The following data, extracted from the study represent the daily numbers of late cancellations and no-shows for a random sample of 10 days at a hotel: 18, 16, 16, 16, 14, 18, 16, 18, 14, 19. c) Count the number of measurements that actually fall within the interval(-28, +2s) and express the interval count as a percentage of total number of measurements. at least 75% 100%

Holt Mcdougal Larson Pre-algebra: Student Edition 2012
Holt Mcdougal Larson Pre-algebra: Student Edition 2012
1st Edition
ISBN:9780547587776
Author:HOLT MCDOUGAL
Publisher:HOLT MCDOUGAL
Chapter11: Data Analysis And Probability
Section11.5: Interpreting Data
Problem 1C
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3. Travelers who have no intention of showing up often fail to cancel their hotel reservations in a timely manner. These travelers are known, in the parlance of the hospitality trade, as "no-shows". To protect against no-shows and late cancellations, hotels invariably overbook rooms. A recent study examined the problems of over-booking rooms in the hotel industry. The following data, extracted from the study represent the daily numbers of late cancellations and no-shows for a random sample of 10 days at a hotel: 18, 16, 16, 16, 14, 18, 16, 18, 14, 19. c) Count the number of measurements that actually fall within the interval(-28, +2s) and express the interval count as a percentage of total number of measurements. at least 75% 100% 68% 95%
Transcribed Image Text:3. Travelers who have no intention of showing up often fail to cancel their hotel reservations in a timely manner. These travelers are known, in the parlance of the hospitality trade, as "no-shows". To protect against no-shows and late cancellations, hotels invariably overbook rooms. A recent study examined the problems of over-booking rooms in the hotel industry. The following data, extracted from the study represent the daily numbers of late cancellations and no-shows for a random sample of 10 days at a hotel: 18, 16, 16, 16, 14, 18, 16, 18, 14, 19. c) Count the number of measurements that actually fall within the interval(-28, +2s) and express the interval count as a percentage of total number of measurements. at least 75% 100% 68% 95%
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