China
Africa
Explore chapters and articles related to this topic
Quality Management Practices
Published in Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla, Total Quality Management (TQM), 2020
Sunil Luthra, Dixit Garg, Ashish Agarwal, Sachin K. Mangla
A check sheet is a simple and useful tool of quality control. A check sheet is a planned document that is used to record all the activities that happen during a specific period of time on the shop floor. It collects real-time data at the location where the process has occurred. It provides the required information in a quick, easy, and efficient manner. A check sheet is a quality control tool that allows the collection and compilation of data in an efficient manner. It is a simple form in which all the information has been recorded in an organised manner by simply putting a tick mark in the column. A check sheet contains all the information regarding the process, including the location. A check sheet is a fundamental quality control tool that collects information in the required format. The check sheet shows how frequently an event occurs during a defined period of time. A check sheet, in short, can be defined as a structured form for collecting and analysing real-time data. It is used for the collection and organisation of the real-time data in a systematic process. It can collect and analyse both qualitative and quantitative data.
Assessment of heavy metal pollution with different indices in Süreyyabey dam lake in Turkey
Published in Chemistry and Ecology, 2023
Şeyda Erdoğan, Gökben Başaran Kankılıç, Merve Seyfe, Ülkü Nihan Tavşanoğlu, Şenol Akın
Prior to the analysis, normality and homogeneity of variance assumptions were checked by a Shapiro-Wilk and Levene’s test, respectively. To determine the differences between heavy metal concentration among stations and seasons, a one-way analysis of variance (ANOVA) was run followed by a Tukey post-hoc test. When the data have not met the assumptions of normality, the Kruskall-Wallis test was applied and for the pairwise comparisons, Dunn’s test was applied. To analyse the significant differences between water and sediment Mann – Whitney U test was employed. The principal component analysis (PCA) is very common for detecting the heavy metal pollution sources in the field; thus, PCA was run to identify the possible contributing source of heavy metals [5,39]. All analyses were performed at a 0.05 statistical significance level. All statistical analysis was performed using R statistical software (R Statistical Software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria)). The geographic information system (GIS) is a very common approach to delineate the source of heavy metal pollution in the field. To obtain the GIS-based spatial distribution maps, a free and open-source QGIS was used.
Factor analysis of maintenance decisions for warranty pavement projects using mixed-effects logistic regression
Published in International Journal of Pavement Engineering, 2022
Xiaohua Luo, Feng Wang, Ningning Wang, Xin Qiu, Farshad Amini, Jueqiang Tao
The statistical analysis of variance (ANOVA) was conducted to analyse the deviance of the mixed-effects logistic regression model used in the study. The analysis of deviance was implemented to compare the maximum likelihood of the observed data under the full model against the maximum likelihood of the data under a model with reduced explanatory variables, and the results are displayed in Table 7. The residual deviance value of 2583.16 in the first line of the right column represents the difference of variability in sum of squares of data between the null model (a model with only the intercept) and the full model (the current model) and indicates how the mixed-effects logistic regression model adopted in the study is working against the null model. In the column the rest of the values below the first line show that the residual deviance decreases when adding each variable, which means involving the added variable in the model could increase the accuracy. Specifically, adding location, distress number, distress amount at low severity level, distress amount at medium severity level, and distress amount at high severity level significantly reduces the residual deviance. However, the inclusion of other variables seems to have only limited improvement in model performance although average rutting depth and average IRI both have low p-values in Table 6.
Objectively measured sedentary behaviour in overweight and obese prepubertal children: challenging the school
Published in International Journal of Environmental Health Research, 2020
Assumpta Ensenyat, Noemi Serra-Paya, Lucía Sagarra-Romero
Descriptive data are presented as the mean and standard deviation or confidence interval. The independent sample t-test or the non-parametric Mann–Whitney U test was used to analyse the differences between groups based on gender. A t-test for paired samples or the Wilcoxon signed-rank test was run to analyse differences in free-living movement for each participant within a day or across the days of the week. The standardised effect size was calculated as the mean difference between groups (gender, time period) divided by the pooled standard deviation. Values of 0.2–0.5 represent small changes, 0.5–0.8 moderate changes and >0.8 large changes. Linear regression analysis was conducted to explore the relationship between free-living movement behaviour and age. The normality of residuals was checked by Kolmogorov–Smirnov test. Significance level was set at α = 0.05. All statistical analyses were performed using the software Statistical Package for the Social Sciences (version 20.0, SPSS Institute Inc., Chicago, IL, USA).