Search results for "Bivariate analysis"
showing 10 items of 94 documents
Perceived benefits and constraints in vehicle automation: Data to assess the relationship between driver's features and their attitudes towards auton…
2019
This data article examines the association driver's features, perceptions and attitudes towards autonomous vehicles (AVs). The data was collected using a structured self-administrable and online-based questionnaire, applied to a full sample of 1205 Spanish drivers. The data contains 4 parts: the full set of bivariate correlations between study variables; descriptive statistics and graphical trends for each main study variable according to gender, age group and city/town size; and, finally, the dataset for further explorations in this regard. For more information, it is convenient to read the full article entitled “Perceived safety and attributed value as predictors of the intention to use a…
Protective role of mindfulness, self-compassion and psychological flexibility on the burnout subtypes among psychology and nursing undergraduate stud…
2021
Abstract To explore the relationship between mindfulness, self-compassion and psychological flexibility, and the burnout subtypes in university students of the Psychology and Nursing degrees, and to analyse possible risk factors for developing burnout among socio-demographic and studies-related characteristics. Design Cross-sectional study conducted on a sample of 644 undergraduate students of Nursing and Psychology from two Spanish universities. Methods The study was conducted between December 2015 and May 2016. Bivariate Pearson's correlations were computed to analyse the association between mindfulness facets, self-compassion and psychological flexibility, and levels of burnout. Multivar…
Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.
2007
A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…
Heritability of refractive astigmatism: a population-based twin study among 63- to 75-year-old female twins.
2013
PURPOSE: To examine the heritability of refractive astigmatism in older women. METHODS: Astigmatism was measured with an autorefractor in 88 monozygotic and 82 dizygotic female twin pairs aged 63 to 75 years. The prevalence and distribution of astigmatism and polar values J0 and J45 were estimated by standard statistical methods. Bivariate maximum likelihood model fitting was used to estimate genetic and environmental variance components using information from both eyes. RESULTS: Mean astigmatism of the more astigmatic eye was 0.93 diopters (D; SD ±0.58). Astigmatism of at least 0.25 D, 0.5 D, 0.75 D, or 1.0 D in either eye was present in 99.7%, 88.5%, 66.5%, and 46.2% of cases, respectivel…
Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.
2005
A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sampl…
Need of causal analysis for assessing phase relationships in closed loop interacting cardiovascular variability series
2003
The phase spectra obtained by the classical closed loop autoregressive model (2AR) and by an open loop autoregressive model (ARXAR) were compared to shed light on the need of introducing causality in the assessment of the delay between RR and arterial pressure oscillations. The reliability of the two approaches was tested in simulation and real data setting. In simulation, the coupling strength of a bivariate closed loop process was adjusted to obtain a range of working conditions from open to closed loop. In open loop condition, 2AR and ARXAR phases were comparable and in agreement with the imposed delay. In closed loop condition, ARXAR model returned the imposed delays, while 2AR showed a…
Multivariate data analysis and bivariate regression studies applied to comparison of two multi-elemental methods for analysing wine samples
2002
Two inductively coupled plasma mass spectrometry (ICP-MS) methods which permit multi-elemental analysis in wine samples have been compared following two strategies. First, a multivariate tool based on principal component analysis (PCA) was employed for a global (all analytes) qualitative comparison of the two methods. A single plot based on the confidence limits of the Q and T2 PCA model statistics corresponding to the ‘standard’ method results (calibration set) was used to check the comparability of the ‘candidate’ method (test samples). The residual matrix (after test matrix interpolation into the PCA model) gives qualitative information about the nature of the main errors. This approach …
Some approximation properties by a class of bivariate operators
2019
WOS: 000503431300041
Probabilistic characterization of flood hazard using bivariate analysis based on copulas
2014
This study presents an innovative approach to obtain flood hazard maps where hydrological input (synthetic flood design event) to a 2D hydraulic model has been defined by generating flood peak discharges and volumes from a bivariate statistical analysis, through the use of copulas. Synthetic hydrographs were generated by means two different approaches: an indirect one, where rainfall were generated by a stochastic bivariate rainfall generator to be entered a distributed conceptual rainfall-runoff model that consisted of a soil moisture routine and a flow routing routine; and a direct one, where stochastic generation of flood peaks and flow volumes have been obtained via copulas, which descr…
Job strain in public transport drivers: Data to assess the relationship between demand-control model indicators, traffic accidents and sanctions
2018
This Data in Brief (DiB) article examines the association between the Job Demand-Control (JDC) model of stress and traffic safety outcomes (accidents and sanctions) in public transport drivers (n = 780). The data was collected using a structured self-administrable questionnaire composed of measurements of work stress (Job Content Questionnaire), and demographics (professional driving experience, hours and days working/driving per week). The data contains 4 parts: descriptive statistics, bivariate correlations between the study variables, analysis of variance (ANOVA) and Post-Hoc comparisons between drivers classified different quadrants of the JDC model. For further information, it is conve…