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Demand for stereo headphones and MP3 players for joggers has caused Nina Industries to grow almost 50 percent over the past year. The number of joggers continues to expand, so Nina expects demand for headsets to also expand, because, as yet, no safety laws have been passed to prevent joggers from wearing them. Demand for the players for last year was as follows: MONTH DEMAND (UNITS) January 4,000 February 4,100 March 3,800 April 4,200 May 4,850 June 4,500 July 5,150 August 4,750 September 5,250 October 5,550 November 6,150 December 5,850   a. Using linear regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Exhibit 3.7. (picture below)(Do not round intermediate calculations. Round your answers to 2 decimal places.) Month Forecast january   february   march   april   may   june   july   august   september   october   november   december     b. To be reasonably confident of meeting demand, Nina decides to use 2 standard errors of estimate for safety. How many additional units should be held to meet this level of confidence? (Do not round intermediate calculations. Round your answer to the nearest whole number.)

Demand for stereo headphones and MP3 players for joggers has caused Nina Industries to grow almost 50 percent over the past year. The number of joggers continues to expand, so Nina expects demand for headsets to also expand, because, as yet, no safety laws have been passed to prevent joggers from wearing them. Demand for the players for last year was as follows: MONTH DEMAND (UNITS) January 4,000 February 4,100 March 3,800 April 4,200 May 4,850 June 4,500 July 5,150 August 4,750 September 5,250 October 5,550 November 6,150 December 5,850   a. Using linear regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Exhibit 3.7. (picture below)(Do not round intermediate calculations. Round your answers to 2 decimal places.) Month Forecast january   february   march   april   may   june   july   august   september   october   november   december     b. To be reasonably confident of meeting demand, Nina decides to use 2 standard errors of estimate for safety. How many additional units should be held to meet this level of confidence? (Do not round intermediate calculations. Round your answer to the nearest whole number.)

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter13: Regression And Forecasting Models
Section13.3: Simple Regression Models
Problem 6P: Do the sales prices of houses in a given community vary systematically with their sizes (as measured...
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Demand for stereo headphones and MP3 players for joggers has caused Nina Industries to grow almost 50 percent over the past year. The number of joggers continues to expand, so Nina expects demand for headsets to also expand, because, as yet, no safety laws have been passed to prevent joggers from wearing them. Demand for the players for last year was as follows:

MONTH DEMAND (UNITS)
January 4,000
February 4,100
March 3,800
April 4,200
May 4,850
June 4,500
July 5,150
August 4,750
September 5,250
October 5,550
November 6,150
December 5,850

 

a. Using linear regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Exhibit 3.7. (picture below)(Do not round intermediate calculations. Round your answers to 2 decimal places.)

Month

 Forecast
january  
february  
march  
april  
may  
june  
july  
august  
september  
october  
november  
december  

 

b. To be reasonably confident of meeting demand, Nina decides to use 2 standard errors of estimate for safety. How many additional units should be held to meet this level of confidence? (Do not round intermediate calculations. Round your answer to the nearest whole number.)

Least Squares Regression Analysis (1) (2) (3) (4) (5) (6) y tX y Y 1 600 600 1 360,000 801.3 2 1,550 3,100 2,402,500 1,160.9 3 1,500 4,500 2,250,000 1,520.5 4 1,500 6,000 16 2,250,000 1,880.1 5 2,400 12,000 25 5,760,000 2,239.7 3,100 18,600 36 9,610,000 2,599.4 7 2,600 18,200 49 6,760,000 2,959.0 8 2,900 23,200 64 8,410,000 3,318.6 3,800 34,200 81 14,440,000 3,678.2 10 4,500 45,000 100 20,250,000 4,037.8 11 4,000 44,000 121 16,000,000 4,397.4 12 4,900 58,800 144 24,010,000 4,757.1 78 33,350 268,200 650 112,502,500 t= 6.5 b = 359.6154 y = 2,779.17 Therefore, Y = 441.67 + 359.6t a = 441.6667 S = 363.9 yt
Transcribed Image Text:Least Squares Regression Analysis (1) (2) (3) (4) (5) (6) y tX y Y 1 600 600 1 360,000 801.3 2 1,550 3,100 2,402,500 1,160.9 3 1,500 4,500 2,250,000 1,520.5 4 1,500 6,000 16 2,250,000 1,880.1 5 2,400 12,000 25 5,760,000 2,239.7 3,100 18,600 36 9,610,000 2,599.4 7 2,600 18,200 49 6,760,000 2,959.0 8 2,900 23,200 64 8,410,000 3,318.6 3,800 34,200 81 14,440,000 3,678.2 10 4,500 45,000 100 20,250,000 4,037.8 11 4,000 44,000 121 16,000,000 4,397.4 12 4,900 58,800 144 24,010,000 4,757.1 78 33,350 268,200 650 112,502,500 t= 6.5 b = 359.6154 y = 2,779.17 Therefore, Y = 441.67 + 359.6t a = 441.6667 S = 363.9 yt
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