Statistical analyses tools

In terms of statistical methods, our NPPI Tool Library has several statistical analysis tools to capture innovation opportunities at processes that are likely to drift and become out of control or processes that execute with random failure. 

Different tools are required for each item. Items 1 and 2 require the customer s input (voice of the customer). The tools include data-gathering tools, like focus groups and surveys, and statistical analysis tools, like conjoint and regression analysis. Measurements may have to be developed requiring the use of gage studies. 

One bioinformatics company that offers specialized siRNA software tools is called Ocimum Biosolutions. Their software, iRNAwiz, provides an environment for the design of successful siRNA molecules and is composed of several components. These components include a siRNA Search tool, a BLAST tool, a Motif Search tool, a Stemloop search, and a Statistical Analysis tool [32]. They claim the combination of these tools will result in the design of siRNA molecules with high efficiency. 

Pattern recognition The ability to analyze and interpret patterns, oftentimes with the help of statistical analysis tools, to discriminate and/or identify analytes. See also chemometric analysis. 

Analyze. Having identified the who and what of this problem, we now target the where, when, and why of the defects in the process. We use appropriate statistical analysis tools, scatter plots, SPC and SQC, Input/Output matrixes, hypothesis testing, and the like, and attempt to accurately understand what is happening in the process. 

This collaborative database contains information on numerous usual metabolites. Including mass spectrometry data on metabohtes, it is the default database for automatic searches of the LC-MS statistical analysis tool Xcms online . 

Statistical analysis can range from relatively simple regression analysis to complex input/output and mathematical models. The advent of the computer and its accessibiUty in most companies has broadened the tools a researcher has to manipulate data. However, the results are only as good as the inputs. Most veteran market researchers accept the statistical tools available to them but use the results to implement their judgment rather than uncritically accepting the machine output. 

The role of quality in reliability would seem obvious, and yet at times has been rather elusive. While it seems intuitively correct, it is difficult to measure. Since much of the equipment discussed in this book is built as a custom engineered product, the classic statistical methods do not readily apply. Even for the smaller, more standardized rotary units discussed in Chapter 4, the production runs are not high, keeping the sample size too small for a classical statistical analysis. Run adjustments are difficult if the run is complete before the data can be analyzed. However, modified methods have been developed that do provide useful statistical information. These data can be used to determine a machine tool s capability, which must be known for proper machine selection to match the required precision of a part. The information can also be used to test for continuous improvement in the work process. 

Probabilistic CA. Probabilistic CA are cellular automata in which the deterministic state-transitions are replaced with specifications of the probabilities of the cell-value assignments. Since such systems have much in common with certain statistical mechanical models, analysis tools from physics are often borrowed for their study. Probabilistic CA are introduced in chapter 8. 

In summary, chemical force microscopy appears to be a powerful tool for the study of adhesion on the nanoscale provided that issues such as the detrimental influence of mechanical effects, substrate roughness, packing density, unknown tip radius, and reliance on statistical analysis can somehow be resolved in a consistent and logical way. 

Several statistical, quality management, and optimization data analysis tools, aimed at exploring records of measurements and uncover useful information from them, have been available for some time. However, all of them require from the user a signifieant number of assumptions and a priori decisions, which determine in a very strict manner the validity of the final results obtained. Furthermore, these classical tools are guided... 

A brief study of the available data related to limits of inflammability in Part Two shows that these parameters are subject to high experimental uncertainty. For a large number of substances, the experimental values are widely dispersed. When they are submitted to quality estimation using statistical tools, in many cases they reveal that it is impossible to use them with confidence. The examples of difficulties raised by the statistical analysis of the LEL data can be multiplied. 

In recent decades, the development of chemical, biochemical, and biological techniques has allowed the creation of analytical tools which can be used to facilitate the identification of the mechanisms involved in neoplastic transformation. Animal models remain, however, the most widely used approach of investigation. Cancer bioassays are usually conducted in rodents (rats and mice) and the experimental protocol takes 18-24 months and it is followed by extensive histopathological and statistical analysis. The procedure is time and... 

Biochips produce huge data sets. Data collected from microarray experiments are random snapshots with errors, inherently noisy and incomplete. Extracting meaningful information from thousands of data points by means of bioinformatics and statistical analysis is sophisticated and calls for collaboration among researchers from different disciplines. An increasing number of image and data analysis tools, in part freely accessible ( ) to academic researchers and non-profit institutions, is available in the web. Some examples are found in Tables 3 and 4. 

Quantile probability plots (QQ-plots) are useful data structure analysis tools originally proposed by Wilk and Gnanadesikan (1968). By means of probability plots they provide a clear summarization and palatable description of data. A variety of application instances have been shown by Gnanadesikan (1977). Durovic and Kovacevic (1995) have successfully implemented QQ-plots, combining them with some ideas from robust statistics (e.g., Huber, 1981) to make a robust Kalman filter. 

Statistical analysis is a very useful tool for evaluating the effects of treatment on many developmental and reproductive toxicity parameters. For some parameters, such as maternal body weight changes, fetal weight, and horizontal activity in an open field, the comparison to the concurrent control is the primary consideration and, assuming adequate group size, the investigator relies heavily on the results of appropriate statistical analyses to interpret differences from control. 

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