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Evaluation and Incorporation of Uncertainties in Geotechnical Engineering
Published in Chong Tang, Kok-Kwang Phoon, Model Uncertainties in Foundation Design, 2021
In the characterization of geotechnical model factors, both descriptive and inferential statistics are used to provide some useful insights. A typical statistical process includes three main aspects: (1) a sample is selected from the population; (2) the sample is described by means of summary statistics and/or frequency distribution; (3) with these descriptive statistics, inferential procedures are applied to reason about the population. When more than one variable is considered, bivariate and multivariate analysis can be used to describe the relation between pairs of variables, such as graphical representation via scatter plot and quantitative measure of dependence, including correlation (e.g. Pearson’s r when both variables are continuous or Spearman’s ρ if one or both are not) and covariance. The theoretical framework is primarily founded on the classical normal or multivariate normal distribution. There are other frameworks, such as copulas, but their applications remain somewhat restricted at present (e.g. Tang et al. 2013a, b).
Statistical Methods for Reproducible Data Analysis
Published in Asis Kumar Tripathy, Chiranji Lal Chowdhary, Mahasweta Sarkar, Sanjaya Kumar Panda, Cognitive Computing Using Green Technologies, 2021
Sambit Kumar Mishra, Mehul Pradhan, Rani Aiswarya Pattnaik
For categorical variable:Count – Absolute frequency of each category in a categorical variable.Count % – Proportion of different categories in a categorical variable expressed in %.Bivariate analysis: Analysis of the relationship between two variables. This includes the phenomenon when two variables are studied together for their empirical relationship.
Applied Statistics
Published in Vinayak Bairagi, Mousami V. Munot, Research Methodology, 2019
Varsha K. Harpale, Vinayak K. Bairagi
The method of estimating relation or correlation or measure of association between two variables is a bivariate analysis. Normally this correlation ranges between –1 and 1, this negative or positive relationship between the variables states direction of correlation. If the correlation of variable is more away from 0 or more toward –1 and 1 then it represents more perfect the relationship between the independent and dependent variations is called degree or extent of correlation. Measures of association and statistical significance that are used may vary as per the level of measurement of the variables analyzed [5].
Grit, motivational belief, self-regulated learning (SRL), and academic achievement of civil engineering students
Published in European Journal of Engineering Education, 2022
Hector Martin, Renaldo Craigwell, Karrisa Ramjarrie
The Pearson product-moment or bivariate correlation expresses the strength of the relationship between two variables (George and Mallery 2011). Correlation values range from −1 to +1, where the magnitude of the value indicates the strength of the relationship and the direction (negative or positive) reflects the relationship’s nature. A correlation coefficient of zero indicates no relationship between the variables at all. The assumption of using this method is that the two measured variables are approximately normally distributed (George and Mallery 2011). The Durbin-Watson statistic is used to test the remainder of the assumptions for sample suitability for parametric evaluation and to ensure the residuals are independent (or uncorrelated). This statistic can vary from 0 to 4, with the optimal being 2. All values were within the range of 1 and 3, rendering the analysis valid (Stevens 2012). Cook’s Distance statistic for each participant was determined. No value was over 1 to indicate significant outliers, which may place undue influence on the model. Scatter plots were also examined.
Characterization of indoor settled dust and investigation of indoor air quality in different micro-environments
Published in International Journal of Environmental Health Research, 2018
Veerendra Sahu, Suresh Pandian Elumalai, Sneha Gautam, Nitin Kumar Singh, Pradyumn Singh
Pearson bivariate correlation analysis is one of the simplest tools to identify degree of linear relationship between two different variables. In this study, we used bivariate Pearson correlation coefficient for the elemental composition analysis of indoor settled dust samples collected from S1, S2, and S3 site. Table 1 shows the correlation values for different elements with other elements present at that site. Data of elemental composition of indoor dust samples are also given in supplementary Information (Table S1). We found strong correlation of C with S (r > 0.99), O with Si and Ca (r > 0.874), O with Fe (r > 0.9). This clearly indicates the effect of coal and biomass burning and fly ash in the indoor environments (Saini et al. 2014; Singh et al. 2014).
Managing the organic municipal waste in Palestine: Linking policy, practice, and stakeholders’ attitude toward composting
Published in Journal of the Air & Waste Management Association, 2023
Majed Ibrahim Al-Sari’, A. K. Haritash
The bivariate analysis is used to assess the relationship between two variables, and examine the effect of each influencing factor independently. The findings of the bivariate analysis are presented in Tables 1–3. The analysis of the results of these factors is described as follows.