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A Corpus Based Quantitative Analysis of Gurmukhi Script
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
Gurjot Singh Mahi, Amandeep Verma
is used to represent the calculated value of the Kendall rank correlation coefficient. and define the number of concordant and discordant pairs in a bivariate environment. As followed by the other two mentioned correlation coefficients, value also ranges between , i.e., . The number of values in the dataset is defined using .
Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective
Published in Abbas Rajabifard, Greg Foliente, Daniel Paez, COVID-19 Pandemic, Geospatial Information, and Community Resilience, 2021
Nadia Abbaszadeh Tehrani, Abolfazl Mollalo, Farinaz Farhanj, Nooshin Pahlevanzadeh, Milad Janalipour
Pearson is a linear approach to measure correlation among two variables, which was frequently used in previous researches. The range of values in Pearson is changing between -1 and 1. Values approaching -1 and 1 show high correlation between variables [53]. Spearman is another correlation estimation method which assesses monotonic relationships between variables [54]. The Kendall rank correlation coefficient is another way to measure correlation of two variables [55]. To assess the significance level of the three correlation approaches, P-values were also computed.
Dependence and Independence: Structures, Testing, and Measuring
Published in Albert Vexler, Alan D. Hutson, Xiwei Chen, Statistical Testing Strategies in the Health Sciences, 2017
Albert Vexler, Alan D. Hutson, Xiwei Chen
Classical dependence measures, such as the Pearson, Spearman, and Kendall rank correlation coefficients, and their accompanying tests of independence, technically target only specific subsets of dependence structures. For example, Pearson’s correlation coefficient (Pearson 1920) measures the degree of the linear correlation, while the Spearman correlation coefficient (Spearman 1904) focuses on a monotone relationship between two random variables, both of which may fail in the broader sense when the underlying dependence structure is nonmonotone (Embrechts et al. 2002). Furthermore, Pearson and Spearman correlation coefficients may not be well suited for measuring dependence across several well-known cases, for example, in the models Y = 1/X or Y = ε/Xk, k = 1, 2, where e is a random variable and X, Y are dependent in an inverse manner. In the second case, E(XY) may not even exist. The Kendall rank correlation coefficient quantifies the concordance in ranks between pairs of random variables (Kendall 1938, 1948) but shows relatively low power in many cases compared to the Pearson and Spearman correlation coefficients (Mudholkar and Wilding 2003). Herein lies the difficulty of interpreting the classical correlation coefficients as general dependence measures. Kendall and Stuart (1979) claimed that “in general, the problem of joint variation is too complex to be comprehended in a single coefficient p. 308.”
Calpain activity in plasma of patients with essential tremor and Parkinson’s disease: a pilot study
Published in Neurological Research, 2021
Zamira M Muruzheva, Dmitry S Traktirov, Alexander S Zubov, Nina S Pestereva, Maria S Tikhomirova, Marina N Karpenko
Statistical analysis was performed using Statistica-8.0 (StatSoft, USA). The distribution of the data was assessed for normality using the Kolmogorov test. All values were represented as the mean ± standard error of the mean (SEM) or the median (lower and upper quartile). The chi-square test was used to compare the proportions. Correlation analyses were performed using Kendall rank correlation coefficient. Differences between three groups were established using univariate analysis of variance followed by Tukey’s test or Kruskal–Wallis one-way analysis of variance. If the connection between PD and ET severity and calpain content in plasma was seen multivariate analysis of variance followed by Tukey’s test was applied. A two-sided p-value less than 0.05 was considered statistically significant.
Could interleukin-33 and its suppressor of tumorigenicity 2 (ST2) receptor have a role in cervical human papillomavirus (HPV) infections?
Published in Gynecological Endocrinology, 2019
Nur Gozde Kulhan, Mehmet Kulhan, Merve Aydin, Umit Nayki, Cenk Nayki, Pasa Ulug, Nahit Ata, Cuma Mertoglu, Aytekin Cikman, İlyas Sayar, Can Turkler
Statistical package program SPSS version 20.0 (IBM Corp. Released 2011, IBM SPSS Statistics for Windows, Armonk, NY; IBM firm) was wont to appraise the information. Variables mean ± standard deviation and median (maximum–minimum) percentage and frequency values are used. In addition, the homogeneity of the variances from the preconditions of the parametric tests was checked by the ‘Levene’ test. The assumption of normality was checked by the ‘Shapiro-Wilk’ test. ‘Student’s t Test’ was used once constant quantity check stipulations were provided for variations between the 2 groups; ‘Mann–Whitney-U check’ was used once constant quantity test stipulations were not provided. One-way ANOVA for three or more group comparisons and the Bonferroni-Dunn test from Kruskal–Wallis and multiple comparison tests were used when not provided by the Tukey HSD test from multiple comparison tests. McNemar Bowker’s Test, Fisher’s Exact Test, Chi-Square Test, Sensitivity and selectivity calculations, positive predictive value, negative predictive value were calculated when categorical data were analyzed. When the expected eyewear is less than 20%, the values are determined by ‘Monte Carlo Simulation Method’ to include these views in the analysis. The relationship between two variables was evaluated by the Kendall rank correlation coefficient when the parametric test prerequisites were not met. Statistical significance was accepted as p<.05 and p<.01.
Impaired thiol-disulphide homeostasis in patients with axonal polyneuropathy
Published in Neurological Research, 2018
Gonul Vural, Hesna Bektas, Sadiye Gumusyayla, Orhan Deniz, Murat Alışık, Ozcan Erel
The statistics software SPSS 20 (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.) was used for the evaluation of data. The average ± standard error values were used for the variables. In addition, the homogeneity of variance, which is one of the preconditions of the parametric tests, was checked with Levene’s test. The normality hypothesis was evaluated with the Shapiro–Wilk test. Because it is used to evaluate the differences between two groups, the Student’s t-test was used if the parametric test preconditions were fulfilled, and the Mann–Whitney U test was used if the parametric test preconditions were not fulfilled. The relationship between two variables was evaluated with the Kendall rank correlation coefficient if the parametric test preconditions were not fulfilled. Here, p < 0.05 was considered to be the statistical significance for the Levene and Shapiro–Wilk tests of the parametric test pre-conditions. In the statistical methods used (Student’s t-test, Mann–Whitney U, Kendall rank correlation), the items marked with a single asterisk (*) refer to the significance level of p < 0.05 and the items marked with two asterisk signs (**) refer to the significance level of p < 0.01 in the related tables.