Topic
P–P plot
About: P–P plot is a research topic. Over the lifetime, 60 publications have been published within this topic receiving 2604 citations.
Papers published on a yearly basis
Papers
TL;DR: This paper describes and discusses graphical techniques, based on the primitive empirical cumulative distribution function and on quantile (Q-Q) plots, percent (P-P) plots and hybrids of these, which are useful in assessing a one-dimensional sample, either from original data or resulting from analysis.
Abstract: SUMMARY This paper describes and discusses graphical techniques, based on the primitive empirical cumulative distribution function and on quantile (Q-Q) plots, percent (P-P) plots and hybrids of these, which are useful in assessing a one-dimensional sample, either from original data or resulting from analysis. Areas of application include: the comparison of samples; the comparison of distributions; the presentation of results on sensitivities of statistical methods; the analysis of collections of contrasts and of collections of sample variances; the assessment of multivariate contrasts;_ and the structuring of analysis of variance mean squares. Many of the objectives and techniques are illustrated by examples. This paper reviews a variety of old and new statistical techniques based on the cumulative distribution function and its ramifications. Included in the coverage are applications, for various situations and purposes, of quantile probability plots (Q-Q plots), percentage probability plots (P-P plots) and extensions and hybrids of these. The general viewpoint is that of analysis of data by statistical methods that are suggestive and constructive rather than formal procedures to be applied in the light of a tightly specified mathematical model. The technological background is taken to be current capacities in data collection and highspeed computing systems, including graphical display facilities. It is very often useful in statistical data analysis to examine and to present a body of data as though it may have originated as a one-dimensional sample, i.e. data which one wishes to treat for purposes of analysis, as an unstructured array. Sometimes this is applicable to ' original' data; even more often such a viewpoint is useful with 'derived' data, e.g. residuals from a model fitted to the data. The empirical cumulative distribution function and probability plotting methods have a key role in the statistical treatment of one-dimensional samples, being of relevance for summarization and palatable description as well as for exposure and inference.
1,411 citations
TL;DR: In this paper, the normal probability plot correlation coefficient (NPC) was used as a test statistic for the composite hypothesis of normality, and the proposed test statistic is conceptnally simple, is compntationally convenient, and is readily extendible to testing non-normal distributional hypotheses.
Abstract: This paper introdLlces the normal probability plot correlation coefficient as a test statistic in complete samples for the composite hypothesis of normality. The proposed test statistic is conceptnally simple, is compntationally convenient, and is readily extendible to testing non-normal distributional hypotheses. An empirical power strldy shows that the normal probability plot correlation coefficient, compares favorably with 7 other normal test statistics. Percent points are tabulated for n = 3(l)50(5)100.
917 citations
TL;DR: In this paper, the authors examined the use of the probability probability probability plot (p-p plot) as a method for comparing treatment effects and compared it with the quantile-quantile plot (q-q plot), which is an alternative means of describing treatment effects.
Abstract: This article examines the use of the probability-probability plot (p-p plot) as a method for comparing treatment effects. To begin in the context of three examples the p-p plot is contrasted with the quantile-quantile plot (q-q plot), which is an alternative means of describing treatment effects. In these examples it is shown that p-p plots representing different experimental conditions or patient populations allow scale-invariant comparisons of treatment effects but q-q plots do not; that the presentation of the treatment effect by the p-p plot is not obscured by outliers, whereas it may be in the q-q plot; and that the p-p plot encompasses information in the control distributions that is important for the assessment of treatment effects but that is not incorporated in the q-q plot. Theoretical considerations are presented that show that under appropriate assumptions, the p-p plot is a maximal invariant and contains all the information necessary to make scale-invariant comparisons of treatment e...
81 citations
TL;DR: In this paper, the statistical properties of the estimators for the number of trees/ha (density) for fixed-radius plot and n-tree distance sampling were compared for both the random and clustered spatial patterns, and the variance of the plot sampling density estimator for the clustered pattern will always be greater than for that of the random spatial pattern.
Abstract: We computed and compared the statistical properties of the estimators for the number of trees/ha (density) for fixed-radius plot and n-tree distance sampling In forests with random spatial patterns, n-tree distance sampling density estimators are at least as precise as those of plot sampling if the fixed-radius plot size is less than the ratio of (n - 2) and the expected density, where n is the number of trees included at an n-tree location A similar result holds for the clustered forest, where the ratio is multiplied by a factor involving a constant of heterogeneity If the expected number of trees per plot and the plot sizes are the same for both the random and clustered spatial patterns, the variance of the plot sampling density estimator for the clustered pattern will always be greater than for that of the random spatial pattern
58 citations
TL;DR: In this paper, an empirical equation for characterizing the heterogeneity of non-isotropic fields is proposed, which is an extension of Fairfield Smith's (1938) empirical law describing heterogeneity in isotropic fields.
36 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2016 | 4 |
| 2015 | 2 |
| 2014 | 4 |
| 2013 | 2 |
| 2012 | 6 |
| 2011 | 1 |