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Basic statistics Statistical thinking Cost of quality Measurement system analysis Critical parameters Critical to quality

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There are two types of statistics, descriptive and inferential. The descriptive statistics summarize the historical data (Gupta et al., 2007). Basic descriptive statistical analysis consists of the mean, median, mode, range, variance, and standard deviation (Gupta et al., 2007). Also measuring the cost of quality is critical because high variations and inconsistencies can cause high cost and waste valuable resources. Inferential statistics is based on analysis of the sample to infer performance of the process (Gupta et al., 2007). It is generally concerned with the source of the data and seeks to make generalizations beyond the data at hand. Inferential statistics can include regression analysis and hypothesis testing among many others.

The WSDK Toolset Rational Application Developer (RAD)

The analysis phase begins the convergence of possibilities toward the root cause of the problem (Gupta et al., 2007). Key analysis tools consist of multi-vary analysis, cause and effect diagrams, regression analysis, and failure modes and effects analysis (FMEA). The cause and effect diagram is used to identify the source of the problem of the process. The cause and effect diagram shows each branch of the process and the inputs related. From the diagram one is then able to notice the different contributors to the problem or process. The regression analysis helps to identify causes and show correlations between variables. Often when solving problems involving many variables most have an inherent relationship. Regression is a way to estimate variable y if there is x amount of variable z. There are independent and dependent variables. Dependent variables are the responses and the independent variables are the causes. Relationships are not always deterministic; that is, x does not always give the same value for y (Walpole and Myers, 1993). As a result relationships cannot be exact, and regression methods are used to predict relationships. Multi-vary analysis is a way to reduce the number of potential causes by removing the trivial ones (Gupta et al., 2007). Many data sets have hidden or not easily recognized similarities, patterns, or structures (Graham, 1993). Different techniques that can be used to address this are cluster analysis, two-factor analysis of variance, and three-factor experiments.

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The failure mode and effect analysis is used to show the potential failures reducing the critical failures of the process (Gupta et al., 2007). Usually when creating a failure mode and effect analysis each potential failure is ranked from 1 to 10 in relation to the damage the failure could cause. Then solutions are thought of to prevent each failure, putting priority on the highest ranked failure.

Our preferred implementation for JAX-RPC and JSR 109 is the WSDK Toolset Therefore, we discuss it first WSDK stands for WebSphere Services Development Kit and is based on the Apache SOAP engine, Axis This toolkit includes a number of command-line tools to help develop the Web Services using either one of the two approaches: top-down or bottom-up The two command-line tools for the bottom-up approach are Bean2WebService and EJB2WebService The first is a utility for creating relevant artifacts to expose a bean as a Web Service In addition, this utility creates all the files required for a service client This utility is built on top of the Apache Axis command-line tool The second utility, EJB2WebService, converts a session EJB into a Web Service using a bottom-up approach This utility also wraps an Apache Axis commandline tool The third utility included in the WSDL toolset is WSDL2WebService.