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The following mathematics (in matrix form) were applied to calculate the con dence intervals multivariable regression models: y0 t / 2 s x 0 ( X X ) 1 x 0 < Y x < y0 + t / 2 s x 0 ( X X ) 1 x 0
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where y0 = predicted mean annual solid waste generation t /2 = t value at the speci ed con dence level and n k 1 degrees of freedom s = unbiased estimate of residual mean square x0 = condition vector of independent variable values for prediction X = matrix of xi values that give rise to the response yi The performance parameters were established using a t /2 = 3. A value of 3 corresponds to approximately 3 above or below the mean annual waste generation tonnage. A performance parameter of 3 was chosen to balance the two types of errors associated with statistical quality control, type I and type II. Type I error is the risk of a point falling beyond performance parameters, indicating an out of control condition when no assignable cause is present (Montgomery, 1997). Type II error is the risk of a point falling between the performance parameters when the process is really out of control. By using a 3 the probability of type I error is 0.27 percent; that is an incorrect out of control signal will be generated only 27 out of 10,000 data points. A 3 limit is typically used in statistical quality control and was applied to this research. The performance parameters for the 20 waste groups involved a similar statistical basis used with the x (x-bar) chart. The x-bar chart is commonly used in manufacturing environments to track and control the quality of products or services. The following list displays the three values that were calculated to establish the performance parameters:
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1 The expected or mean solid waste quantity waste 2 Upper performance parameter 3 Lower performance parameter
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The latter two were calculated from the previous equation. The bene ts of this method are: ease of calculations, ease of programming, ability to benchmark, standardization, objectivity, and con dentiality. The con dentiality aspect of the method involves its usage. This method is applicable to Internet systems and may be easily available from a Web site for private use by companies to quickly and privately evaluate their waste generation performance. The performance parameters identify high and low waste generators based on research speci c data. The performance parameters developed will aid manufacturing and service companies in evaluating their solid waste generation in comparison with the waste group standards established from this research. The performance parameters determine when a company is out of control in
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relation to industry standards when they are producing signi cantly above or below the determined waste generation levels for their industry, represented by their respective waste group. After establishing the performance parameters and determining if a company is out of control several actions can be taken. First, if the performance parameter indicates the company is below the determined waste generation level, the company should be praised so that best practices can be replicated for similar companies to reduce waste levels. However, if the performance parameter indicates the company is above the determined waste generation level, the company should be investigated so that better waste reduction efforts can be taken up. Performance parameter analyses were conducted on the 20 waste groups to investigate similarities of highest 5 percent and lowest 5 percent of waste generators for each waste group. This was conducted to identify trends in terms of independent variables and materials generated among the highest and lowest waste generators. Manufacturing waste groups explained by three signi cant variables (number of employees, land ll disposal cost per ton, and ISO 14001 certi cation) and nonmanufacturing waste groups explained by one signi cant variable (number of employees) were analyzed separately. Tables 17.1 to 17.4 display the results. The columns labeled higher/lower than average material generation lists the waste material composition percentages that were signi cantly higher (more/less than 5 percent) than the averages for the waste group discussed. An analysis of the higher/lower than average waste generators is discussed at the conclusion of this chapter. Case studies discussing the application of the performance parameters to two companies are provided later in the book. As shown by the tables, the manufacturing waste groups (three signi cant variables) showed some convincing similarities among high waste generators. Speci cally, high waste generators were not ISO 14001 certi ed, had lower than average disposal costs, and generated larger than average composition percentages of wood and cardboard (container waste). Low waste generators were opposite, in that most were ISO 14001 certi ed, had average disposal costs, and generated lower than average amounts of wood and cardboard. Low waste generators also had higher recycling levels than high waste generators. This is to be expected because recycling levels were correlated with disposal cost and ISO 14001 certi cation as discussed earlier. Nonmanufacturing waste groups (one signi cant variable) also showed similarities between high waste generators, but not as strong as for manufacturing waste groups. Most high waste generators produced higher amounts of mixed of ce paper (MOP) and some packing materials. High waste generators also had lower recycling levels than low waste generators. Again, as in the manufacturing waste groups, low waste generators displayed opposite trends. The next chapter discusses the integration of the environmental model, which includes the performance parameters discussed in this section. 20 discusses two case studies, which apply the performance parameters and provide speci c details on waste generation performance.
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298 NUMBER OF COMPANIES IN THE HIGHEST 5% OF WASTE GENERATORS ISO 14001 CERTIFICATION OVERALL RECYCLING LEVEL LANDFILL DISPOSAL COSTS (DOLLARS PER TON) HIGHER THAN AVERAGE MATERIAL GENERATION VERSUS WASTE GROUP AVERAGES
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TABLE 17.1 HIGHEST FIVE PERCENT WASTE GENERATORS IN THE MANUFACTURING WASTE GROUPS (THREE SIGNIFICANT VARIABLES) BASED ON PERFORMANCE PARAMETERS
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