			Home About us Contact

Probability Plot (probability + plot)

Distribution by Scientific Domains

Chemistry	57%
Medical Sciences	28%

Selected Abstracts

Probability plots based on Student's t -distribution

ACTA CRYSTALLOGRAPHICA SECTION A, Issue 4 2009
Rob W. W. Hooft
The validity of the normal distribution as an error model is commonly tested with a (half) normal probability plot. Real data often contain outliers. The use of t -distributions in a probability plot to model such data more realistically is described. It is shown how a suitable value of the parameter , of the t -distribution can be determined from the data. The results suggest that even data that seem to be modeled well using a normal distribution can be better modeled using a t -distribution. [source]

Heart Rate Variability Fraction,A New Reportable Measure of 24-Hour R-R Interval Variation

ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 1 2005
Maciej Sosnowski M.D.
Background: The scatterplot of R-R intervals has several unique features. Its numerical evaluation may produce a new useful index of global heart rate variability (HRV) from Holter recordings. Methods: Two-hundred and ten middle-aged healthy subjects were enrolled in this study. The study was repeated the next day in 165 subjects. Each subject had a 24-hour ECG recording taken. Preprocessed data were transferred into a personal computer and the standard HRV time-domain indices: standard deviation of total normal R-R intervals (SDNN), standard deviation of averaged means of normal R-R intervals over 5-minute periods (SDANN), triangular index (TI), and pNN50 were determined. The scatterplot area (0.2,1.8 second) was divided into 256 boxes, each of 0.1-second interval, and the number of paired R-R intervals was counted. The heart rate variability fraction (HRVF) was calculated as the two highest counts divided by the number of total beats differing from the consecutive beat by <50 ms. The HRVF was obtained by subtracting this fraction from 1, and converting the result to a percentage. Results: The normal value of the HRVF was 52.7 ± 8.6%. The 2,98% range calculated from the normal probability plot was 35.1,70.3%. The HRVF varied significantly with gender (female 48.7 ± 8.4% vs male 53.6 ± 8.6%, P = 0.002). The HRVF correlated with RRI (r = 0.525) and showed a similar or better relationship with SDNN (0.851), SDANN (0.653), and TI (0.845) than did the standard HRV measures with each other. Bland-Altman plot showed a good day-by-day reproducibility of the HRVF, with the intraclass correlation coefficient of 0.839 and a low relative standard error difference (1.8%). Conclusion: We introduced a new index of HRV, which is easy for computation, robust, reproducible, easy to understand, and may overcome the limitations that belong to the standard HRV measures. This index, named HRV fraction, by combining magnitude, distribution, and heart-rate influences, might become a clinically useful index of global HRV. [source]

The Groningen Longitudinal Glaucoma Study.

ACTA OPHTHALMOLOGICA, Issue 4 2009

Abstract. Purpose:, We aimed to determine prospectively the incidence of abnormal test results on frequency doubling perimetry (FDT), the nerve fibre analyser (GDx) and standard automated perimetry (SAP) in a cohort of glaucoma suspect patients with normal findings for all these tests at baseline. Methods:, Seventy glaucoma suspect patients were followed prospectively for 4 years with SAP (Humphrey field analyser 30-2 SITA Fast), FDT (C-20 full-threshold) and GDx (Version 2.010) in a clinical setting. All patients had normal baseline test results on SAP, FDT and GDx. After the follow-up period, the number of patients who converted (whose test results changed from normal at baseline to reproducibly abnormal during follow-up) were counted for each technique and then compared. The cut-off point for FDT was > 1 depressed test-point p < 0.01 in the total deviation probability plot; the cut-off point for GDx was the Number > 29. Results:, Of the 70 glaucoma suspect patients, three converted on FDT, 14 on GDx and six on SAP. These proportions are significantly different for GDx versus SAP (p = 0.033) and GDx versus FDT (p = 0.002), but not for FDT versus SAP (p = 0.256). Conclusions:, The most frequent finding after a 4-year follow-up was conversion on GDx. [source]

The lognormal distribution is not an appropriate null hypothesis for the species,abundance distribution

JOURNAL OF ANIMAL ECOLOGY, Issue 3 2005
MARK WILLIAMSON
Summary 1Of the many models for species,abundance distributions (SADs), the lognormal has been the most popular and has been put forward as an appropriate null model for testing against theoretical SADs. In this paper we explore a number of reasons why the lognormal is not an appropriate null model, or indeed an appropriate model of any sort, for a SAD. 2We use three empirical examples, based on published data sets, to illustrate features of SADs in general and of the lognormal in particular: the abundance of British breeding birds, the number of trees > 1 cm diameter at breast height (d.b.h.) on a 50 ha Panamanian plot, and the abundance of certain butterflies trapped at Jatun Sacha, Ecuador. The first two are complete enumerations and show left skew under logarithmic transformation, the third is an incomplete enumeration and shows right skew. 3Fitting SADs by ,2 test is less efficient and less informative than fitting probability plots. The left skewness of complete enumerations seems to arise from a lack of extremely abundant species rather than from a surplus of rare ones. One consequence is that the logit-normal, which stretches the right-hand end of the distribution, consistently gives a slightly better fit. 4The central limit theorem predicts lognormality of abundances within species but not between them, and so is not a basis for the lognormal SAD. Niche breakage and population dynamical models can predict a lognormal SAD but equally can predict many other SADs. 5The lognormal sits uncomfortably between distributions with infinite variance and the log-binomial. The latter removes the absurdity of the invisible highly abundant half of the individuals abundance curve predicted by the lognormal SAD. The veil line is a misunderstanding of the sampling properties of the SAD and fitting the Poisson lognormal is not satisfactory. A satisfactory SAD should have a thinner right-hand tail than the lognormal, as is observed empirically. 6The SAD for logarithmic abundance cannot be Gaussian. [source]

Polymorphism in iodotris(tri- p -tolylphosphine)silver(I)

ACTA CRYSTALLOGRAPHICA SECTION B, Issue 2 2009
Gertruida J. S. Venter
The reaction of silver(I) iodide with tri(p -tolyl)phosphine in MeCN solution in 1:3 molar ratio yields a polymorph of the complex of the formula [AgI{P(4-MeC6H4)3}3], with the Ag atom in a distorted tetrahedral environment. A polymorphic structure of this complex (a) is compared with previously published crystal structures (b), determined at different temperatures. The two polymorphs are compared using r.m.s. overlay calculations as well as half-normal probability plots. [source]