Statistical programs

The higher the dimensionality, the more acute the need for a visual check before statistical programs are indiscriminately applied. 

How critically interdependent matrix and analytical methods can be is illustrated in the example of the analysis of a soil sample. Table 7.1 shows the method dependent certified values for some common trace elements. The soil had been subjected to a multi-national, multi-laboratory comparison on a number of occasions (Houba et al. 1995) which provided extensive data. The data was subjected to a rigorous statistical program, developed for the USEPA by Kadafar (1982). This process allowed the calculation of certified values for a wide range of inorganic analytes. Uniquely, for the soil there are certified values for four very different sample preparation methods, as follows ... 

Since many equations and analysis procedures have been described in the literature, we present here just a few of the most commonly used equations. The solutions to these equations are obtained using a nonlinear curve fitting routine found in many commercially available statistical programs. 

It is best to begin by describing the environment in which a statistical programmer works in the pharmaceutical industry. Then we explore the fundamental principles that should guide you in your day-to-day work. These principles permeate all of the tasks that you do on a daily basis and, if kept in mind, they will keep you from going astray in your statistical programming duties. 

How clean the data must be depends on the importance of the data. Critical analysis variables must be clean, so this is where the site and data management groups should focus their resources. If the data are dirty at the time of statistical analysis, many inefficient and costly workarounds may need to be applied in the statistical programming, and the quality of the data analysis could suffer. However, if a variable is not important to the statistical analysis, then it is better to save the expense of cleaning that variable. 

GENDER STATISTICS PROGRAMMING GET SIMPLE FREQUENCY COUNTS FOR GENDER. proc freq... 

Braun, W. J., Murdoch, D. J. A First Course in Statistical Programming with R. Springer, New York, 2007. 

Measurement of precision. Measurement of data quality is valuable for both the analyst and the data user. Least-squares curve-of-best-fit statistical programs usually provide some information on precision (correlation coefficient, standard error of estimate). However, these are not sufficiently quantitative and often overstate the quality parameters of the data. 

The data listed in Table 5.2 are the concentrations of a drug in plasma and come from a test problem of the BMDP statistical program package (ref. 19). 

Results were analyzed by nested mixed-model ANOVA s using general linear procedures, in the MINITAB 15 statistical program. Nested mixed-model ANOVA was used when multiple leaves per tree and multiple trees per treatment were available. Additional analyses were linear and quadratic regressions (performed in MINITAB 15 and Excel), and when significant differences occurred, means were compared using Student s t-test or nested mixed-model ANOVA. 

Individual results versus averages or high and low values Data evaluation Conclusion Data evaluation Statistical programs Criteria... 

This chapter is aimed at introducing the concepts of biostatistics and informatics. Statistical analysis that evaluates the reliability of biochemical data objectively is presented. Statistical programs are introduced. The applications of spreadsheet (Excel) and database (Access) software packages to analyze and organize biochemical data are described. 

Use spreadsheet (Excel) or a statistical program to solve the following problems (1-8) after reviewing pertinent topics discussed in biochemistry texts (Garrett and Grisham,1999 Van Holde et ah, 1998 Voet and Voet, 1995 Zubay, 1998). 

Statistical analysis for these assays can be performed using many statistical programs including a twotailed student t-test or a KruskalWallis nonparametric analysis of variance on averaged triplicate values. 

A final comment addresses the use of statistics. If judiciously applied, statistics is an invaluable tool for finding values of coefficients that best fit experimental data. However, caution is called for in two respects A good statistical correlation provides no guarantee that the equation or model used is indeed correct. In complex systems, the evaluation may converge on a false optimum. Also, primitive statistics programs do not distinguish between random scatter and systematic deviations. As an illustration, Figure 3.11 shows a comparison of two first-order concentration plots of data with approximately the same statistical deviation from... 

Many statistical programs (such as Minitab, SigmaXL, and IMP) automatically calculate control limits for the various types of Process Behavior Charts. If you re curious or want to perform your own calculations, see the resources listed at the end of this technique. 

Tel. 44-993-830385, fax 44-993-830966 Nonbonded force field probing for sites of interaction between small mole-cules/functional groups and 3D protein structures. GRIN helps create input. Output from GRID can be prepared in formats suitable for display in molecular modeling packages or for analysis in statistical programs VAX, Evans Sutherland, and UNIX workstations. 

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