Large-Scale Analysis and Testing

Table 7.1 Standard industrial classification codes 7 Large-Scale Analysis and Testing... 

The rest of the book is organized as follows. Chapter 2 reviews the theoretical bases and proposes the research model. Chapter 3 discusses the antecedents of supply chain collaboration. Chapter 4 explores the nature and characteristics of supply chain collaboration. Chapter 5 focuses on the consequences of supply chain collaboration—collaborative advantage and firm performance. Chapter 6 describes structured interview and Q-sort. Chapter 7 presents large-scale analysis and hypotheses testing. Chapter 8 provides a discussion of research and managerial insights. 

Economic Analysis is vital to justify the implementation of new diagnostic tests into resource limited health-care systems. POCTs are often more expensive on a test-by-test basis due to the scaling and portability of devices and not benefiting from the efficiency of large-scale multiplex laboratory testing of multiple samples. However, POCT can have benefits further down the clinical pathway such as those described in Table 2.1. These indirect cost savings can only be established by performing a comparative evaluation of the clinical pathway with and without the test. 

In the following section, the results of large-scale analysis for each construct will be reported and discussed. The coding for items is shown in Appendices C and D. The instruments that will be used in structural analysis and hypothesis testing after the large-scale study are shown in Appendix F. 

Frequency domain performance has been analyzed with goodness-of-fit tests such as the Chi-square, Kolmogorov-Smirnov, and Wilcoxon Rank Sum tests. The studies by Young and Alward (14) and Hartigan et. al. (J 3) demonstrate the use of these tests for pesticide runoff and large-scale river basin modeling efforts, respectively, in conjunction with the paired-data tests. James and Burges ( 1 6 ) discuss the use of the above statistics and some additional tests in both the calibration and verification phases of model validation. They also discuss methods of data analysis for detection of errors this last topic needs additional research in order to consider uncertainties in the data which provide both the model input and the output to which model predictions are compared. 

If accurate design/analysis data are necessary (e.g., Vjmm, Vfup), it is possible to obtain the information needed by undertaking the appropriate bench-type tests and large-scale test-design procedures, similar to the approaches employed for pneumatic conveying (Pan and Wypych, 1992a Arnold et al., 1994). 

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