27 papers · ranked by Valyu relevance
Laura C. Guglielmetti, Fabio Faber-Castell, Lukas Fink, Raphael N. Vuille-dit-Bille
'Raphael N. Vuille-dit-Bille'] Background Statistic scripts are often made by mathematicians and cryptic for clinicians or non-mathematician scientists. Nevertheless, almost all research projects necessitate the application of some statistical tests or at least an understanding thereof. The present review aims on…
Angeliki Papana, Nikolay K. Vitanov
The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on…
M Harshvardhan, Pritam Ranjan
Pearson correlation is the default measure of association in most statistical software, yet it is only appropriate for pairs of continuous variables with a linear relationship. When variables are binary, ordinal, or categorical, specialized methods (e.g., point-biserial, polychoric, tetrachoric, and Cramér's~$V$) may…
Alex E. Yuan, Wenying Shou
In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series, and determine whether the correlation is statistically significant (i.e. unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series…
Robert M Flight, Praneeth S Bhatt, Hunter NB Moseley
Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In both cases, the correlation value will be impacted based on a…
Constantine Stalikas, Vasilios Sakkas
In this tutorial review, we provide a guiding reference on the good practice in building calibration and correlation experiments, and we explain how the results should be evaluated and interpreted. The review centers on calibration experiments where the relationship between response and concentration is expected to be…
Caroline Cannistra, Linh Hoang, Alex E. Yuan, Wenying Shou
Correlation analyses using ecological time series can indicate phenomena such as interspecific interactions or an environmental factor that affects several populations. However, methodological choices in these analyses can significantly impact the results, potentially leading to spurious correlations or missed true…
Ming Luo, S. Radhakrishnan, Sagar Kamarthi
Study on Surface Roughness in Finish Turning Authors: ['Ming Luo' 'S. Radhakrishnan' 'Sagar Kamarthi'] | 1 | INTRODUCTION | 3 | | --- | --- | --- | | 2 | THE CONCEPT OF CORRELATION BETWEEN TWO VARIABLES | 3 | | | 2.1 Pearson Correlation Coefficient | 8 | | | 2.2 Spearman's Rank Correlation Coefficient | 9 | | | 2.3…
Florian Griessenberger, Wolfgang Trutschnig, Robert R. Junker
Correlations belong to the standard repertoire of ecologists for quantifying the strength of dependence between two random variables. Classical dependence measures are usually not capable of detecting non-monotonic or non-functional dependencies. Furthermore, they completely fail to detect asymmetry and direction in…
Zenon Gniazdowski
The article investigates the possibility of measuring the strength of a linear correlation relationship between nominal data and numerical data. Correlation coefficients for variables coded with real numbers as well as for variables coded with complex numbers were studied. For variables coded with real numbers…
Miroslava Cuperlovic-Culf, Anuradha Surendra, Irina Alecu, Abdullah Mahdi + 2 more
Introduction Data-driven determination is a powerful approach for unbiased investigation of the functional relationships in biomolecular networks. Such networks can be inferred from omics data, where correlation analysis is a commonly used method. However, the correlation values depend strongly on sample variability…
Kontemeniotis Nikolaos, Vargiakakis Rafail, Tsagris Michail
Distance correlation is a measure of dependence between two paired random vectors or matrices of arbitrary, not necessarily equal, dimensions. Unlike Pearson correlation, the population distance correlation coefficient is zero if and only if the random vectors are independent. Thus, distance correlation measures both…
Martin Winistörfer, Ivan Zhdankin
—Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the…
Miguel A. García‐Pérez
The analysis of multiple bivariate correlations is often carried out by conducting simple tests to check whether each of them is significantly different from zero. In addition, pairwise differences are often judged by eye or by comparing the p-values of the individual tests of significance despite the existence of…
Md. Aktar Hossain, Saima Sultana
In silico analysis is a powerful technique to identify better therapeutic interventions. Molecular docking is widely used to screen ligands through analysing binding affinities for target receptors. In this study we screened ligands for two proteins which are potential drug targets: deoxyuridine triphosphate…
Authors not listed
Chiral 2D metal halide perovskites (MHPs) hold promise for next-generation spin-optoelectronic applications. While still in early-stage development, significant efforts have focused on optimizing the dissymmetry factor (g), particularly in terms of their circular dichroism (gabs), which quantifies the chiroptical…
Alex E. Yuan, Wenying Shou
In disciplines from ecology to neuroscience, researchers analyze correlations between pairs of nonstationary time series to infer relationships among variables. This often involves a statistical test to determine whether an observed correlation is stronger than expected under the null hypothesis of independence.…
Alex E. Yuan, Wenying Shou
Researchers frequently analyze correlations between pairs of time series by determining whether an observed correlation is stronger than expected under the null hypothesis of independence. However, the time series are often nonstationary, with statistical properties that change over time, thereby making standard tests…
Souvik Manna, Diptendu Roy, Sandeep Das, Biswarup Pathak
Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important…
René Lehmann, Bodo Vogt
Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and costs. Grant funding depends on the quality of psychotherapeutic measures. Bipolar Likert scales yield compositional data because any order of magnitude of agreement towards…
Alexander Muacevic, John R Adler, Alessandro Rovetta
During my experience as an author, peer reviewer, and editor during COVID-19, I have encountered - and committed - various errors related to the interpretation and use of statistical measures and tests. Primarily concerning health sciences such as epidemiology, infodemiology, and public health, the evidence used to…
Joseph Davies, David Pattison, Jonathan Hirst
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including…
Lindley R. Slipetz, Jiaxing Qiu, Siqi Sun, Teague R. Henry
analytic approach Authors: ['Lindley R. Slipetz' 'Jiaxing Qiu' 'Siqi Sun' 'Teague R. Henry'] Nonlinear relations between variables, such as the curvilinear relationship between childhood trauma and resilience in patients with schizophrenia (Wang, Zhang, Zheng, Li, & Zhou, 2023) and the moderation relationship between…
Authors not listed
Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand. In this work, we present a Python-based Workflow for Interpretability Scoring using matched molecular Pairs (WISP).…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Authors not listed
Mechanical agitation (stirring) is a cornerstone of organic synthesis, but has received little scientific attention due to its “obvious” role in facilitating reactions. A very recent study by Huang and coworkers compared the isolated yields in approximately 600 paired stirred and unstirred reactions, across a range of…