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In #15, the regression equation should be: In μ= ß₁ + ẞ₁ Int+ ẞ2x where x = 0 for Company A and x = 1 for Company B. 15. Employee accident numbers in two large manufacturing companies (A & B) were compiled over a two-year period with the intention of com- paring rates between the two companies and looking for trends over time. The unit of time used was two months, with the results over two years appearing in Table 7.12. The Poisson distribution is a natural one to consider for counting accident data of this type. Although no information is provided here on exposure of the companies to accidents (such as number of employees), it would normally be of interest. For example, if Company B had twice as many employees as Company A (and otherwise working conditions were similar), then we might natu- rally expect the accident rate in Company B to be approximately twice that of Company A. Exposure information of this type can be taken into consideration in GLM modeling through the use of offset parameters. TABLE 7.12 Industrial accidents over time in companies A and B. Time Period (Unit = 2 Months) Company A Company B 12 19 14 8 1 2 3 4 5 6 10 18 11 6 7 8 9 10 11 12 19 13 19 17 21 16 21 15 23 27 19 29 37 27 35 26 PROBLEMS 263 A plot of the data reveals a slight upward trend in accidents over time for both companies. Consider and analyze a multiplicative model where the accident rate is proportional to time for each company, or in other words, that logµ = 0 + ẞ₁ logt + xc where xc is an indicator for company.

In #15, the regression equation should be: In μ= ß₁ + ẞ₁ Int+ ẞ2x where x = 0 for Company A and x = 1 for Company B. 15. Employee accident numbers in two large manufacturing companies (A & B) were compiled over a two-year period with the intention of com- paring rates between the two companies and looking for trends over time. The unit of time used was two months, with the results over two years appearing in Table 7.12. The Poisson distribution is a natural one to consider for counting accident data of this type. Although no information is provided here on exposure of the companies to accidents (such as number of employees), it would normally be of interest. For example, if Company B had twice as many employees as Company A (and otherwise working conditions were similar), then we might natu- rally expect the accident rate in Company B to be approximately twice that of Company A. Exposure information of this type can be taken into consideration in GLM modeling through the use of offset parameters. TABLE 7.12 Industrial accidents over time in companies A and B. Time Period (Unit = 2 Months) Company A Company B 12 19 14 8 1 2 3 4 5 6 10 18 11 6 7 8 9 10 11 12 19 13 19 17 21 16 21 15 23 27 19 29 37 27 35 26 PROBLEMS 263 A plot of the data reveals a slight upward trend in accidents over time for both companies. Consider and analyze a multiplicative model where the accident rate is proportional to time for each company, or in other words, that logµ = 0 + ẞ₁ logt + xc where xc is an indicator for company.

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
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In #15, the regression equation should be: In μ= ß₁ + ẞ₁ Int+ ẞ2x where x = 0 for Company A and x = 1 for Company B.
Transcribed Image Text:In #15, the regression equation should be: In μ= ß₁ + ẞ₁ Int+ ẞ2x where x = 0 for Company A and x = 1 for Company B.
15. Employee accident numbers in two large manufacturing companies (A & B) were compiled over a two-year period with the intention of com- paring rates between the two companies and looking for trends over time. The unit of time used was two months, with the results over two years appearing in Table 7.12. The Poisson distribution is a natural one to consider for counting accident data of this type. Although no information is provided here on exposure of the companies to accidents (such as number of employees), it would normally be of interest. For example, if Company B had twice as many employees as Company A (and otherwise working conditions were similar), then we might natu- rally expect the accident rate in Company B to be approximately twice that of Company A. Exposure information of this type can be taken into consideration in GLM modeling through the use of offset parameters. TABLE 7.12 Industrial accidents over time in companies A and B. Time Period (Unit = 2 Months) Company A Company B 12 19 14 8 1 2 3 4 5 6 10 18 11 6 7 8 9 10 11 12 19 13 19 17 21 16 21 15 23 27 19 29 37 27 35 26 PROBLEMS 263 A plot of the data reveals a slight upward trend in accidents over time for both companies. Consider and analyze a multiplicative model where the accident rate is proportional to time for each company, or in other words, that logµ = 0 + ẞ₁ logt + xc where xc is an indicator for company.
Transcribed Image Text:15. Employee accident numbers in two large manufacturing companies (A & B) were compiled over a two-year period with the intention of com- paring rates between the two companies and looking for trends over time. The unit of time used was two months, with the results over two years appearing in Table 7.12. The Poisson distribution is a natural one to consider for counting accident data of this type. Although no information is provided here on exposure of the companies to accidents (such as number of employees), it would normally be of interest. For example, if Company B had twice as many employees as Company A (and otherwise working conditions were similar), then we might natu- rally expect the accident rate in Company B to be approximately twice that of Company A. Exposure information of this type can be taken into consideration in GLM modeling through the use of offset parameters. TABLE 7.12 Industrial accidents over time in companies A and B. Time Period (Unit = 2 Months) Company A Company B 12 19 14 8 1 2 3 4 5 6 10 18 11 6 7 8 9 10 11 12 19 13 19 17 21 16 21 15 23 27 19 29 37 27 35 26 PROBLEMS 263 A plot of the data reveals a slight upward trend in accidents over time for both companies. Consider and analyze a multiplicative model where the accident rate is proportional to time for each company, or in other words, that logµ = 0 + ẞ₁ logt + xc where xc is an indicator for company.
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