Descriptive statistics and obtained range of all basic numerical measures (N = 939).

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings.

Descriptive statistics and reliabilities of the measures collected at Time 1 (n = 74) and Time 2 (n = 67).

Abstract Objective: to characterize the care given to the elderly by an aeromedical service in the south of Brazil. Method: a descriptive, cross-sectional and quantitative study was performed. The data were collected from reports of care of the elderly between July 2014 and June 2016, and were analyzed using simple descriptive statistics with numerical measures and descriptive charts. Results: of the 1071 care visits performed, 214 (19.9%) were related to occurrences involving the elderly, the majority of whom were male (64.5%) and aged between 60-64 years (29%). The types of care were classified into clinical, trauma or inter-hospital transfer. With respect to clinical care, cardiorespiratory arrest was the most prevalent incident (35.9%), while in trauma care falls were the most frequent occurrence (48.9%). The highest percentage of visits occurred on Sundays (18.7%). In the majority of cases care resulted in referral to reference hospitals (69.63%), followed by visits that evolved to death in the case of 47 elderly persons (21.96%). Conclusion: the findings of the present study represent a relevant contribution to the planning and implementation of care for elderly persons in an emergency situation receiving treatment from an aeromedical service.

Given the subset ; then, the remaining descriptors have at least one pair for which the summary statistic is greater than with descriptors.

Summary of the arguments of the functions in the package EATME.

Agricultural experimentation requires careful selection of the experimental design and model for analyzing treatment data. However, even with rigorous experimental control, the discrepancies between treatments are so subtle that traditional statistical models fail to highlight statistically significant differences that occur in field practice. The incorporation of geotechnologies offers the ability to map agricultural variability, but a gap still exists in the availability of tools designed to map and evaluate the effectiveness of agricultural experiments. To overcome this limitation and promote the wider application of Geographic Information Systems in agriculture, the scope of this study focuses on the development of a resource in QGIS software, aimed at evaluating agricultural experiments using a randomized block design with up to five treatments. The resource developed incorporates spatial interpolation techniques using geostatistical kriging, map generation, and statistics. The study used yield samples from six different crops to identify quantitative and spatial differences between two-treatment experiments in terms of yield gain. The performance evaluation included statistical measures such as Root Mean Square Error (RMSE) and Pearson’s correlation coefficient to validate the accuracy of interpolated surfaces. The results consisted of two surfaces representing the study area treated with each of the treatments, as well as a surface reflecting the yield gain of the reference treatment in relation to the control treatment, accompanied by relevant descriptive statistics measures on this gain surface. The experiments showed that RMSE varied between 4.6% and 69.71%, depending on the crop and treatment, while Pearson’s correlation ranged from −0.16 to 0.86, indicating varying degrees of agreement between interpolated and observed data. The tool successfully generated yield gain maps, allowing spatial visualization of treatment differences, with an accuracy of up to 95.40% in detecting spatial and numerical variations between treatments. The tool, called GEOTrat - Points, offers the flexibility to evaluate agricultural experiments of various designs, encompassing different crops and different quantities of samples, providing both numerical and spatial analysis.

Agricultural experimentation requires careful selection of the experimental design and model for analyzing treatment data. However, even with rigorous experimental control, the discrepancies between treatments are so subtle that traditional statistical models fail to highlight statistically significant differences that occur in field practice. The incorporation of geotechnologies offers the ability to map agricultural variability, but a gap still exists in the availability of tools designed to map and evaluate the effectiveness of agricultural experiments. To overcome this limitation and promote the wider application of Geographic Information Systems in agriculture, the scope of this study focuses on the development of a resource in QGIS software, aimed at evaluating agricultural experiments using a randomized block design with up to five treatments. The resource developed incorporates spatial interpolation techniques using geostatistical kriging, map generation, and statistics. The study used yield samples from six different crops to identify quantitative and spatial differences between two-treatment experiments in terms of yield gain. The performance evaluation included statistical measures such as Root Mean Square Error (RMSE) and Pearson’s correlation coefficient to validate the accuracy of interpolated surfaces. The results consisted of two surfaces representing the study area treated with each of the treatments, as well as a surface reflecting the yield gain of the reference treatment in relation to the control treatment, accompanied by relevant descriptive statistics measures on this gain surface. The experiments showed that RMSE varied between 4.6% and 69.71%, depending on the crop and treatment, while Pearson’s correlation ranged from −0.16 to 0.86, indicating varying degrees of agreement between interpolated and observed data. The tool successfully generated yield gain maps, allowing spatial visualization of treatment differences, with an accuracy of up to 95.40% in detecting spatial and numerical variations between treatments. The tool, called GEOTrat - Points, offers the flexibility to evaluate agricultural experiments of various designs, encompassing different crops and different quantities of samples, providing both numerical and spatial analysis.

Comparison of teaching methods in computer science student learning outcomes—As based on background characteristics: Numerical analysis algorithms.

Agricultural experimentation requires careful selection of the experimental design and model for analyzing treatment data. However, even with rigorous experimental control, the discrepancies between treatments are so subtle that traditional statistical models fail to highlight statistically significant differences that occur in field practice. The incorporation of geotechnologies offers the ability to map agricultural variability, but a gap still exists in the availability of tools designed to map and evaluate the effectiveness of agricultural experiments. To overcome this limitation and promote the wider application of Geographic Information Systems in agriculture, the scope of this study focuses on the development of a resource in QGIS software, aimed at evaluating agricultural experiments using a randomized block design with up to five treatments. The resource developed incorporates spatial interpolation techniques using geostatistical kriging, map generation, and statistics. The study used yield samples from six different crops to identify quantitative and spatial differences between two-treatment experiments in terms of yield gain. The performance evaluation included statistical measures such as Root Mean Square Error (RMSE) and Pearson’s correlation coefficient to validate the accuracy of interpolated surfaces. The results consisted of two surfaces representing the study area treated with each of the treatments, as well as a surface reflecting the yield gain of the reference treatment in relation to the control treatment, accompanied by relevant descriptive statistics measures on this gain surface. The experiments showed that RMSE varied between 4.6% and 69.71%, depending on the crop and treatment, while Pearson’s correlation ranged from −0.16 to 0.86, indicating varying degrees of agreement between interpolated and observed data. The tool successfully generated yield gain maps, allowing spatial visualization of treatment differences, with an accuracy of up to 95.40% in detecting spatial and numerical variations between treatments. The tool, called GEOTrat - Points, offers the flexibility to evaluate agricultural experiments of various designs, encompassing different crops and different quantities of samples, providing both numerical and spatial analysis.

Descriptive of our cohort. For categorical variables: counts of cases (percentage). For numerical: median ( – percentiles).

Summary descriptive statistics for cluster 2 for detection data from Barrow Island between 2009 and 2015.

Summary statistics using numerical variable only for detection data from Barrow Island between 2009 and 2015.

This paper presents a novel extension of the exponentiated inverse Rayleigh distribution called the half-logistic exponentiated inverse Rayleigh distribution. This extension improves the flexibility of the distribution for modeling lifetime data for both monotonic and non-monotonic hazard rates. The statistical properties of the half-logistic exponentiated inverse Rayleigh distribution, such as the quantiles, moments, reliability, and hazard function, are examined. In particular, we provide several techniques to estimate the half-logistic exponentiated inverse Rayleigh distribution parameters: weighted least squares, Cramér-Von Mises, maximum likelihood, maximum product spacings and ordinary least squares methods. Moreover, numerical simulations were performed to evaluate these estimation methods for both small and large samples through Monte Carlo simulations, and the finding reveals that the maximum likelihood estimation was the best among all estimation methods since it comprises small mean square error compared to other estimation methods. We employ real-world lifetime data to demonstrate the performance of the newly generated distribution compared to other distributions through practical application. The results show that the half-logistic exponentiated inverse Rayleigh distribution performs better than alternative versions of the Rayleigh distributions.

Summary statistics for univariate clustering using ckmeans algorithm for log-transformed data for Barrow Island from 2009 to 2015.