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ANALYSIS OF RESULTS AND SUMMARY OF FINDINGS
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TABLE 16.15 CORRELATION ANALYSIS MATRIX BETWEEN DISPOSAL COST AND RECYCLING LEVEL COST RECYCLE
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stronger predictor. This is logical because ISO 14001 is an environmental quality system and ISO 9000 is a general quality system. Finally, the recycling level of a company also did not signi cantly in uence the quantity of solid waste generated (before subtracting recyclables). Research results indicated the recycling level was positively correlated with disposal cost per ton and positively correlated with ISO 14001 certi cation. Cost per ton to dispose and ISO 14001 certi cation had stronger in uences on the quantity of solid waste and were included in the equations. The correlation of recycling level forced this variable out in favor of cost per ton to dispose and ISO 14001 certi cation. Table 16.15 and Fig. 16.16 display the correlation of the recycling level of a company to the cost to dispose for the wood and lumber manufacturing waste group. Table 16.16 and Fig. 16.17 display the correlation of the recycling level of the company to whether or not the company is ISO 14001 certi ed for the wood and lumber manufacturing waste group. These results were typical for other groups.
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0.5 0.45 Overall recycling level 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0 10 20 30 40 Landfill disposal cost ($/ton) 50 60
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Figure 16.16 Scatter diagram (recycling level and land ll disposal costs).
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TABLE 16.16 CORRELATION ANALYSIS MATRIX BETWEEN ISO 14001 CERTIFICATION AND RECYCLING LEVEL ISO RECYCLE
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The following equation was used to calculate the correlation coef cients (Walpole and Myers, 1993): r=b S xy S xx =b S yy S xx S yy
An analysis was conducted to examine the effects of company size on waste generation per employee. The purpose of the test was to examine if larger companies were more or less ef cient than smaller companies in regards to waste generation. For all groups no signi cant correlation was found. Table 16.17 and Fig. 16.18 show the results for the wood and lumber manufacturing waste group. Also notable, nonlinear variables did not aid in the prediction of solid waste. Signi cant relationships were developed using linear variables at the 95 percent con dence level. The next chapter discusses the development of the performance parameters for the 20 waste groups. The regression equations discussed in this chapter served as the basis of these parameters.
Overall recycling level
NO ISO 14001 certification
Figure 16.17 Scatter diagram (recycling level and ISO 14001 certi cation).
ANALYSIS OF RESULTS AND SUMMARY OF FINDINGS
TABLE 16.17 CORRELATION ANALYSIS MATRIX BETWEEN WASTE PER EMPLOYEE AND THE NUMBER OF EMPLOYEES WASTE PER EMPLOYEE
EMPLOYEE
Employee Waste per employee
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30 Annaul solid waste per employee per waste group (tons) 25 20 15 10 5 0 0 20 40 60 Number of employees 80 100
Figure 16.18 Scatter diagram (waste per employee versus the number of employees).
BENCHMARKING AND EVALUATION
Based on the previous chapters waste characterization and signi cant variable analyses, performance parameters were established for the 20 waste groups. The performance parameters were established integrating the theoretical concepts of statistical quality control into the integrated environmental model. This involved applying control limits to the output of the regression models used to determine the signi cant variables that in uence solid waste. This novel approach to quality control allowed for the monitoring and control of solid waste generation of U.S. businesses and government agencies. In particular, con dence intervals were established for the regression model using a control limit as the level of signi cance determined by the t value. These established con dence intervals are the performance parameters for U.S. company waste generation. The following are the single variable upper and lower performance parameter mathematics:
2 2 1 ( x0 x ) 1 ( x0 x ) y0 t / 2 s + < Y x < y0 + t / 2 s + 0 S xx n S xx n
where y0 = predicted value at x 0 t /2 = value of t-distribution with n 2 degrees of freedom. s = unbiased estimate of standard deviation n = sample size x0 = value for independent variable x = mean value for independent variable S xx = ( xi x )2
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