Statistical methods scatter plot

In principle, only the expressions for the correct desorption order should give a straight line at higher temperatures. In practice, however, the experimental scatter, possible inaccuracy in corrections of the output data, inherent departures from the simple model considered (mainly the dependence of Ea on 0), together with a rather strong correlation which can be shown to exist between the functions In [(1 /nB) — (l/nB0) ] and ln[ln(na0) — ln(n ) ], can seriously impair the plot and make the estimate of the desorption order rather dubious. Statistical methods should be helpful in this case, but to our knowledge they have not been employed so far. 

Cluster analysis is far from an automatic technique each stage of the process requires many decisions and therefore close supervision by the analyst. It is imperative that the procedure be as interactive as possible. Therefore, for this study, a menu-driven interactive statistical package was written for PDP-11 and VAX (VMS and UNIX) series computers, which includes adequate computer graphics capabilities. The graphical output includes a variety of histograms and scatter plots based on the raw data or on the results of principal-components analysis or canonical-variates analysis (14). Hierarchical cluster trees are also available. All of the methods mentioned in this study were included as an integral part of the package. 

Examples of mathematical methods include nominal range sensitivity analysis (Cullen Frey, 1999) and differential sensitivity analysis (Hwang et al., 1997 Isukapalli et al., 2000). Examples of statistical sensitivity analysis methods include sample (Pearson) and rank (Spearman) correlation analysis (Edwards, 1976), sample and rank regression analysis (Iman Conover, 1979), analysis of variance (Neter et al., 1996), classification and regression tree (Breiman et al., 1984), response surface method (Khuri Cornell, 1987), Fourier amplitude sensitivity test (FAST) (Saltelli et al., 2000), mutual information index (Jelinek, 1970) and Sobol s indices (Sobol, 1993). Examples of graphical sensitivity analysis methods include scatter plots (Kleijnen Helton, 1999) and conditional sensitivity analysis (Frey et al., 2003). Further discussion of these methods is provided in Frey Patil (2002) and Frey et al. (2003, 2004). 

FiGUfts 4-14 MINITAB output for internal standard method for determining sodium by flame spectrometry. The data are shown In the Worksheet window at lower left. The upper scatter plot Is for the Na intensity versus concentration, and the lower scatter plot is for the ratio of Na intensity to Li Intensity versus concentration. The regression statistics In the Session window show that the internal standard method gives better linearity. 

The RATIO method table (Table I) includes provision for specifying upper and lower limits of integration for both primary and reference bands with the peak area evaluation procedure. The practical limits of the integration can be determined empirically by evaluating a set of spectra stored on microfloppy disks with varying limits set in the appropriate locations in the method table. Optimum limits can be determined from the calibration plots and related error parameters. The calibration plots shown in Figures 4 and 5 indicate that both evaluation procedures, peak height and peak area provide essentially the same level of precision for the linear least squares fit of the data. The error index and correlation coefficients listed on each table are both indicators of the relative scatter in the data from the least squares fit line. The correlation coefficient is calculated as traditionally defined in statistics. 

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