Analysis of Variance. Stated in this manner, the discriminant function problem can be rephrased as a one-way analysis of variance (ANOVA) problem. Specifically, one can ask whether or not two or more groups are significantly different from each other with respect to the mean of a particular variable. To learn more about how one can test for the statistical significance of differences between means in different groups you may want to read the Overview section to ANOVA/MANOVA. However, it should be clear that, if the means for a variable are significantly different in different groups, then we can say that this variable discriminates between the groups.
Multiple Variables. Usually, one includes several variables in a study in order to see which one(s) contribute to the discrimination between groups. In that case, we have a matrix of total variances and covariances; likewise, we have a matrix of pooled within-group variances and covariances. We can compare those two matrices via multivariate F tests in order to determined whether or not there are any significant differences (with regard to all variables) between groups. This procedure is identical to multivariate analysis of variance or MANOVA. As in MANOVA, one could first perform the multivariate test, and, if statistically significant, proceed to see which of the variables have significantly different means across the groups. Thus, even though the computations with multiple variables are more complex, the principal reasoning still applies, namely, that we are looking for variables that discriminate between groups, as evident in observed mean differences.
Significance of discriminant functions. One can test the number of roots that add significantly to the discrimination between group. Only those found to be statistically significant should be used for interpretation; non-significant functions (roots) should be ignored.
As mentioned earlier, discriminant function analysis is computationally very similar to MANOVA, and all assumptions for MANOVA mentioned in ANOVA/MANOVA apply. In fact, you may use the wide range of diagnostics and statistical tests of assumption that are available to examine your data for the discriminant analysis.
Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.
These instructions accompany Applied Regression Modeling by Iain Pardoe, 2nd edition published by Wiley in 2012. The numbered items cross-reference with the "computer help" references in the book. These instructions are based on the "Classic Menus" interface of Statistica 10 for Windows, but they (or something similar) should also work for other versions. Find instructions for other statistical software packages here.
ML, graph/network, predictive, and text analytics, regression, clustering, time-series, decision trees, neural networks, data mining, multivariate statistics, statistical process control (SPC), and design of experiments (DOE) are easily accessed via built-in nodes.
Conclusions: These results show PMS is more frequent in patients with BMI < 25, and less frequent in patients with higher FM (kg) and FM (%). Moreover, significant frequency of PMS was observed in patients with higher FFM and TBW. Such statistical significance was not confirmed in girls with a BMI < 25.
Who should attend? This statistical conference is addressed to statisticians, pharmacometricians, data scientists, regulatory affairs specialists, academia and other experts interested in the field belonging to: Pharmaceutical, and Biotechnology companies, CROs, Universities/Hospitals, Academic Research.
There are many formulas or algorithms for a percentile score. Hyndman and Fan  identified nine and most statistical and spreadsheet software use one of the methods they describe. Algorithms either return the value of a score that exists in the set of scores (nearest-rank methods) or interpolate between existing scores and are either exclusive or inclusive.
Interpolation methods, as the name implies, can return a score that is between scores in the distribution. Algorithms used by statistical programs typically use interpolation methods, for example, the percentile.exc and percentile.inc functions in Microsoft Excel. The Interpolated Methods table shows the computational steps.
R is a free software environment for statistical computing andgraphics. It compiles and runs on a wide variety of UNIX platforms,Windows and MacOS. To download R,please choose your preferred CRAN mirror. 2b1af7f3a8