Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Background Pairs of related individuals are widely used in linkage analysis. Most of the tests for linkage analysis are based on statistics associated with identity by descent (IBD) data. The current biotechnology provides data on very densely packed loci, and therefore, it may provide almost continuous IBD data for pairs of closely related individuals. Therefore, the distribution theory for statistics on continuous IBD data is of interest. In particular, distributional results which allow the evaluation of p-values for relevant tests are of importance. Results A technology is provided for numerical evaluation, with any given accuracy, of the cumulative probabilities of some statistics on continuous genome data for pairs of closely related individuals. In the case of a pair of full-sibs, the following statistics are considered: (i) the proportion of genome with 2 (at least 1) haplotypes shared identical-by-descent (IBD) on a chromosomal segment, (ii) the number of distinct pieces (subsegments) of a chromosomal segment, on each of which exactly 2 (at least 1) haplotypes are shared IBD. The natural counterparts of these statistics for the other relationships are also considered. Relevant Maple codes are provided for a rapid evaluation of the cumulative probabilities of such statistics. The genomic continuum model, with Haldane's model for the crossover process, is assumed. Conclusions A technology, together with relevant software codes for its automated implementation, are provided for exact evaluation of the distributions of relevant statistics associated with continuous genome data on closely related individuals.

This code estimates the generalized continuous-discrete model with a t-distributed error kernel. It also replicates the Monte Carlo study of this paper. If you use any part of this code in any form, please cite this paper.

Many metrics in biological research – from body size to life history timing to environmental metrics – are measured continuously (e.g., body size in grams) but analyzed as categories (e.g., large versus small). The pitfalls of categorization are well-recognized in statistics, but many scientists in the fields of ecology, evolution, and behavior may not be aware of this literature. These fields lack a review of common examples and feasible solutions to avoid the hazards of categorizing continuous data. Our goal was to summarize current practices of categorizing continuous predictors in ecology and evolutionary biology and provide guidance for overcoming those pitfalls. We conducted a mini-review of 72 recent publications in six popular journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common metrics and realistic examples from ecology and evolutionary biolog...

Daily data provide one data value to represent water conditions for the day. Throughout much of the history of the USGS, the primary water data available was daily data collected manually at the monitoring location once each day. With improved availability of computer storage and automated transmission of data, the daily data published today are generally a statistical summary or metric of the continuous data collected each day, such as the daily mean, minimum, or maximum value. Daily data are automatically calculated from the continuous data of the same parameter code and are described by parameter code and a statistic code. These data have also been referred to as “daily values” or “DV”.

The Continuous Data Protection (CDP) Software market is poised for significant growth as organizations increasingly seek robust solutions for safeguarding their critical data assets. CDP software offers real-time backup and recovery solutions, allowing businesses to minimize data loss by capturing changes to data co

Annual descriptive statistics of hourly contaminant concentrations measured continuously by the Quebec Air Quality Monitoring Network.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Provides an aggregate of data for the Office of the Actuary and the Office of Research, Evaluation and Statistics.

Hourly contaminant concentrations measured continuously by the Quebec Air Quality Monitoring Network (RSQAQ). To consult the descriptive statistics of these data, look for the RSQAQ-Statistics-Descriptive-Data-Continuous-Data data. If you have any questions about this data, contact the Info-Air department:. These data exclude those measured on Montreal Island. For detailed instructions on how to open and navigate data files to easily find accurate data, see the [RSQAQ: Navigating Air Quality Data] tutorial (https://www.youtube.com/watch?v=3bLBUOmMEFk).

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist

Summarized data in each Experiment

Continuous Register Statistics: Population by sex, municipalities and country of birth. Annual. Municipalities.

It is natural to regard most animal movement as a continuous-time process, generally observed at discrete times. Most existing statistical methods for movement data ignore this; the remainder mostly use discrete-time approximations, the statistical properties of which have not been widely studied, or are limited to special cases. We aim to facilitate wider use of continuous-time modelling for realistic problems. We develop novel methodology which allows exact Bayesian statistical analysis for a rich class of movement models with behavioural switching in continuous time, without any need for time discretization error. We represent the times of changes in behaviour as forming a thinned Poisson process, allowing exact simulation and Markov chain Monte Carlo inference. The methodology applies to data that are regular or irregular in time, with or without missing values. We apply these methods to GPS data from two animals, a fisher (Pekania [Martes] pennanti) and a wild boar (Sus scrofa), ...

Continuous Population Statistics: Resident population by date, sex and age. Quarterly. National.

Brazil Employment Rate data was reported at 53.900 % in Feb 2019. This records a decrease from the previous number of 54.200 % for Jan 2019. Brazil Employment Rate data is updated monthly, averaging 56.050 % from Mar 2012 (Median) to Feb 2019, with 84 observations. The data reached an all-time high of 57.300 % in Dec 2013 and a record low of 53.100 % in Mar 2017. Brazil Employment Rate data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.