barcode generator in vb net free download Hypothesis Testing Results in Software

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20.2 Hypothesis Testing Results
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Three hypotheses were investigated that were based on the model concept. The investigation of these hypotheses guided the research process. A review of the hypotheses is listed below and discussed in greater detail in the remaining sections:
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1 Similar businesses generate similar waste stream compositions and these busi-
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nesses can be statistically grouped. 2 Signi cant variables can be identi ed that aid in the prediction of annual solid waste generation rates. 3 Solid waste generation performance parameters can be established.
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MODEL SUMMARY AND RECOMMENDATIONS FOR FUTURE RESEARCH
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438 U.S. businesses and government agencies
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Reduce to 65 SIC code groups based on business functions
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Reduce to 22 waste groups based on solid waste stream compositions Variables Investigated Number of employees Working days per year Recycling percentage ISO 9000 certification ISO 14001 certification Disposal cost per ton Location
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Determine significant variables that influence solid waste generation for each waste group through stepwise technique (resulted in the reduction of waste groups from 22 to 20.2 of the Waste Groups did not have a significant relationships to predict annual waste generation at the 95 percent confidence level)
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Develop regression equations to quantify solid waste generation for each waste group. Regression models can predict the solid waste generated by an individual company based on the significant variables. The models also aided in the identification of waste generation trends
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Significant Variables Number of employees ISO 14001 Certification Disposal cost per ton
Establish performance parameters for individual companies to evaluate solid waste generation. This assisted in identifying superior or inferior waste management procedures Companies may use these to evaluate and improve waste generation performance
Summary of research.
20.2.1 CLASSIFICATION OF U.S. BUSINESSES AND GOVERNMENT AGENCIES
The rst hypothesis was successfully validated and proven by research results. U.S. companies and government agencies were rationally grouped by characterizing waste material composition percentages. This was completed by clustering 65 SIC code groups into 20 waste groups by applying multivariate cluster analysis t and associated statistical validation tests. These groups were validated at the 95 percent con dence level, using F-tests and ANOVA.
20.2.2 SIGNIFICANT VARIABLE THAT INFLUENCE SOLID WASTE GENERATION QUANTITIES
The second hypothesis was successfully validated and proven by research results. This was accomplished by applying the stepwise regression method, t tests, and ANOVA. From the 20 solid waste groups established, solid waste quantities were objectively
RESEARCH CONTRIBUTIONS
predicted and characterized using statistical techniques to develop a mathematical model. This was completed using multivariable regression analysis for the 20 waste groups and associated statistical validation tests. The number of employees, land ll disposal costs, and ISO 14001 certi cation were statistically signi cant at the 95 percent con dence level in predicting annual solid waste generation tonnages.
20.2.3 PERFORMANCE EVALUATION AND PREDICTION
The third hypothesis was successfully validated and proved by research results. This was accomplished by developing con dence intervals based on the regression models and testing the outputs with two case studies. Individual company solid waste generation rates were evaluated in a standardized manner by integrating statistical quality control concepts to monitor and control solid waste generation. This was completed by integrating con dence intervals mathematics with the waste prediction models for the 20 waste groups.
20.3 Research Contributions
The largest contribution of this research was the development of the integrated environmental model to predict and evaluate solid waste generation of individual U.S. businesses and government agencies. The analyses conducted combined with the functionality of the model signi cantly increased the understanding of solid waste generation of individual U.S. companies and government agencies. This led to the creation of new knowledge. Speci c contributions are listed below:
Objective and rational grouping of businesses that generation similar quantities and
compositions of solid waste. Ability to predict solid waste generation quantities, material tonnages, and recycling levels of most U.S. businesses and government agencies. Standardized evaluation models to monitor and control solid waste generation of U.S. businesses and government agencies. The integrated environmental model can be programmed on the Internet for con dential usage by businesses to privately evaluate their solid waste generation. Identi cation of waste generation and recycling trends in the United States and further research opportunities based on ndings.
The impact of these contributions will signi cantly aid businesses and environmental regulators to monitor and control solid waste generation. Businesses may now con dentially evaluate their solid waste generation on an Internet-based system and compare their waste generation to industry-speci c benchmarks. This model effectively and ef ciently identi es superior or inferior waste management practices based on generation rates. Regulators now have an integrated, standardized model to assist in determining waste generation rates, compositions, and recycling levels of various
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