statistical procedure
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Safety ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Knut O. Ronold ◽  
Andreas T. Echtermeyer

Fatigue S–N curves provide the number of stress cycles that result in fatigue failure at stress range S and need to be measured for new engineering materials where data are not as readily available as they are for well-characterized and widely used metals. A simple statistical method for the estimation of characteristic fatigue curves defined in terms of lower-tail quantiles in probability distributions of dependent variables is presented. The method allows for the estimation of such quantiles with a specified confidence level, taking account of the statistical uncertainty caused by a limited number of experimental test results available for the estimation. The traditional general approach for estimating characteristic S–N curves by tolerance bounds is complicated and is not much used by engineers. The presented approach allows for calculating the curves with a simple spreadsheet. The only requirement is that the experimental log S data for the S–N curve are fairly uniformly distributed over a finite logS interval, where S denotes the stress range. Experimental fatigue test programs are often designed such that test data fulfil this assumption. Although developed with fatigue of composite laminates in mind, the presented statistical procedure and the presented associated charts are valid for fatigue curve estimation for any material.


2021 ◽  
Author(s):  
Shreya Gupta ◽  
John J. Hasenbein ◽  
Byeongdong Kim

Abstract We develop a method to estimate the quality of processing routes in a wafer fabrication process. Ranking such routes can be useful for identifying the “best” and “worst” routes when making adjustments to recipes. Route categorization is also useful in developing efficient scheduling algorithms. In particular, we propose a method for ranking routes based on count-based metrics such as the number of defects on a wafer. We start with a statistical model to produce a “local” ranking of a tool and then build a “global” ranking via a heuristic procedure. Creating a fully statistical procedure for ranking routes in semiconductor fabrication plants is virtually impossible, given the number of possible routes and the limited data available. Nonetheless, our discussions with working engineers indicate that even approximate rankings are useful for making better operational decisions.


2021 ◽  
Author(s):  
Edward William Legg ◽  
Benjamin George Farrar ◽  
Aleksandra Lazić ◽  
Maleen Thiele ◽  
Dora Kampis ◽  
...  

Null Hypothesis Significance Testing is a statistical procedure widely used in cognitive development research. There is widespread concern that the results of this statistical procedure are misinterpreted and lead to unsubstantiated claims about studies’ outcomes. Two particularly pertinent issues for research on cognitive development are: i) treating a non-significant result as evidence of no difference or no effect, and ii) treating a non-significant result in one group/condition and a significant result in another as evidence of a difference between groups/conditions. The current study focuses on quantifying the extent to which these two issues can be observed in the published literature on cognitive development. To this end, we will systematically search for empirical studies investigating cognitive development in 0-to-16-year-old children that have been published at two time points, namely in 1999 and 2019. For each of the two issues, we will extract information from 300 published articles, 150 per publication year.


2021 ◽  
Vol 10 (6) ◽  
pp. 18
Author(s):  
Titiworada Polyiem

The major objectives of the research were to evaluate and compare expected and actual learning outcomes of teacher students based on National Qualifications Framework for Higher Education in Thailand. The key informants were one hundred and eighteen 1st and 2nd year of teacher students from one university in 2020. The research instrument was a questionnaire on five domains of the national qualifications framework for higher education in Thailand. The data were analyzed by a statistical procedure: percentage, mean, and standard deviation. Paired-samples t-test and content analysis were employed for the study. The research findings indicated that the overall expected learning outcomes and actual learning outcomes regarding the five domains of the national qualifications frameworks were at a high level. The expected learning outcomes and actual learning outcomes regarding the comparison of the expected learning outcomes and actual learning outcomes with the national qualifications framework were not different.


2021 ◽  
Vol 11 (17) ◽  
pp. 8266
Author(s):  
Maria Ragosta ◽  
Mariagrazia D’Emilio ◽  
Luciana Casaletto ◽  
Vito Telesca

Meteorological conditions play a crucial role in air pollution by affecting both directly and indirectly the emissions, transport, formation, and deposition of air pollutants. Extreme weather events can strongly affect surface air quality. Understanding relations between air pollutant concentrations and extreme weather events is a fundamental step toward improving the knowledge of how excessive heat impacts on air quality. In this work, we developed a statistical procedure for investigating the variations in the correlation structure of four air pollutants (NOx, O3, PM10, PM2.5) during extreme temperature events measured in monitoring sites located of Emilia Romagna region, Northern Italy, in summer (June–August) from 2015 to 2017. For the selected stations, Hot Days (HDs) and Heat Waves (HWs) were identified with respect to historical series of maximum temperature measured for a 30-year period (1971–2000). This method, based on multivariate techniques, allowed us to highlight the variations in air quality of study area due to the occurrence of HWs. The examined data, including PM concentrations, show higher values, whereas NOx and O3 concentrations seem to be not influenced by HWs. This operative procedure can be easily exported in other geographical areas for studying effects of climate change on a local scale.


2021 ◽  
Author(s):  
Deepanshu Sharma ◽  
Surya Priya Ulaganathan ◽  
Vinay Sharma ◽  
Sakshi Piplani ◽  
Ravi Ranjan Kumar Niraj

Abstract Background and objectivesMeta-analysis is a statistical procedure which enables the researcher to integrate the results of various studies that were conducted for the same purpose. However, more often than not, researchers find themselves in a position unable to proceed further due to the complexity of the mathematics involved and unavailability of raw data. To alleviate the said difficulty, we are presenting a tool that will enable researchers to process raw data.MethodsThe GUI tool is written in python. The tool offers an automated conversion and obtainment of mean and standard deviation (SD) from median and interquartile range, utilizing the methods offered by Hozo et al. 2005 and Bland 2015.ResultsThe tool is tested on some sample data and validation is performed for Bland method on the data provided in the Bland method publication (14).ConclusionsThe provided tool is an easy alternative for the preparation of input data required for clinical meta-analysis in the required format.


2021 ◽  
Author(s):  
Robert J. Leigh ◽  
Richard A. Murphy ◽  
Fiona Walsh

There is a reproducibility crisis in scientific studies. Some of these crises arise from incorrect application of statistical tests to data that follow inappropriate distributions, have inconsistent equivariance, or have very small sample sizes. As determining which test is most appropriate for all data in a multicategorical study (such as comparing taxa between sites in microbiome studies), we present statsSuma, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) and does not require a user to have any programming experience. This software assesses underlying data structures in a given dataset to advise what pairwise or listwise statistical procedure would be best suited for all data. As some users may be interested in further mining specific trends, statSuma performs 5 different two-tailed pairwise tests (Student's t-test, Welch's t-test, Mann-Whitney U-test, Brunner-Munzel test, and a pairwise Kruskal-Wallis H-test) and advises the best test for each comparison. This software also advises whether ANOVA or a multicategorical Kruskal-Wallis H-test is most appropriate for a given dataset and performs both procedures. A data distribution-vs-Gaussian distribution plot is produced for each taxon at each site and a variance plot between all combinations of 2 taxa at each site are produced so Gaussian tests and variance tests can be visually confirmed alongside associated statistical determinants.


Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 353-365
Author(s):  
Matthieu Saumard ◽  
Bilal Hadjadji

In this paper, we investigate the causality in the sense of Granger for functional time series. The concept of causality for functional time series is defined, and a statistical procedure of testing the hypothesis of non-causality is proposed. The procedure is based on projections on dynamic functional principal components and the use of a multivariate Granger test. A comparative study with existing procedures shows the good results of our test. An illustration on a real dataset is provided to attest the performance of the proposed procedure.


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