Frequency of reported types of studies and use of descriptive and inferential statistics (n = 216).

Ice cream consumption is a global phenomenon with a wide variety of flavors and options available. This article explores interesting statistics about ice cream consumption, including global consumption, per capita consumption, popular flavors, production and sales, frozen treats industry, seasonal consumption, health and dietary trends, and ice cream parlors and chains.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

Basic statistical methods used in medical research by research goal and type of outcome variable.

Australian and New Zealand journal of statistics - ResearchHelpDesk - The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems. In addition, suitable review papers and articles of historical and general interest will be considered. The journal also publishes book reviews on a regular basis. Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Academic Search Elite (EBSCO Publishing) Academic Search Premier (EBSCO Publishing) CompuMath Citation Index (Clarivate Analytics) Current Index to Statistics (ASA/IMS) Journal Citation Reports/Science Edition (Clarivate Analytics) Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS) RePEc: Research Papers in Economics Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) Statistical Theory & Method Abstracts (Zentralblatt MATH) ZBMATH (Zentralblatt MATH)

Feature Articles on Employment and Labour - Statistics on Job Vacancies

Social Scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise, which we try to provide herein, but its application should make the results of quantitative articles more informative and transparent. To illustrate our recommendations, we replicate the results of several published works, showing in each case how the authors' own concl usions can be expressed more sharply and informatively, and, without changing any data or statistical assumptions, how our approach reveals important new information about the research questions at hand. We also offer very easy-to-use Clarify software that implements our suggestions. See also: Unifying Statistical Analysis

The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839) {"references": ["Heller, L., Bl\u00fcmel, I., Cartellieri, S., & Wartena, C. (2016). Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal. Zenodo. http://doi.org/10.5281/zenodo.51562"]}

Following Chapter 4 of my thesis, 3 E.coli strains (MG1655, F022 and ELU39) were analysed using untargeted metabolomics after ~700 generations of evolution in 1 of three conditions: Plasmid free, plasmid carrying and plasmid carrying with antibiotic selection.The following data has been analysed using metaboanalyst

Prevalence of journal-specific features (peer-reviewed journal articles only).

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

✅ The Journal of Community Health Management ISSN - ResearchHelpDesk - The Journal of Community Health Management (JCHM) is open access, double-blind peer-review journal publishing quarterly since 2014. JCHM is proclaimed by Innovative Education and Scientific Research Foundation, print and published by Innovative Publication. It has an International Standard Serial Number (ISSN 2394-272X, e ISSN 2394-2738). JCHM permits authors to self-archive final approval of the articles on any OAI-compliant institutional/subject-based repository. Aim and Scope JCHM is focusing on Community Health which is the branch of the Public Health, it's making people aware and describing their role as determinants of their own and other people’s health in contrast to environmental health which focal point on the physical environment and its impact on people health. It concentrates on the maintenance, protection, and improvement of the health status of population groups and communities. The scope is, therefore, huge covering almost all streams of Community Health Management starting from original research articles, review articles, short communications, and clinical cases as well as studies covering clinical, experimental and applied topics on Community health Management on above subjective areas. The scope of the journal isn't restricted to those subjects however it's the broader coverage of all the newest updates and specialties. Indexing The Journal is an index with Index Copernicus (Poland), Google Scholar, J-gate, EBSCO (USA) database, Academia.edu, CrossRef, ROAD, InfoBase Index, GENAMIC, etc. Keywords Acute Care, Bio-statics, Community Health, Epidemiology and Health Services Research, Health Management, Medicine and Allied branches of Medical Sciences including Health Statistics, Nutrition, Preventive Medicine, Primary Prevention, Primary Health Care, Secondary Prevention, Secondary Healthcare, Tertiary Healthcare.

Through long-term cohort studies, the reoffending phenomenon of 960 parolees returning to society seven years later (2004-2011) was analyzed, and risk factors influencing re-offending were objectively screened through statistical analysis.

The data set contains count of number of articles published by Covenant University Lecturers, in Ota, Ogun State, Nigeria. The dataset contains a sample of 126 lecturers comprising 99 from College of Business and Social Sciences, and 27 from College of Leadership. The dataset include the number of articles published by the lecturers from 2013-2015. The response variable was the number of article produced by lecturers (NOP) which was obtained by counting. Predictors are Gender of lecturers (SEX), male was coded 1, and female as 0, marital status (MS), married was coded as 1 and single as 0, number of children each lecturer have (CHD), years of teaching/lecturing experience (EXP), cadre indicating whether senior or junior lecturer, Assistant lecturer and lecturer II are categorized as Lower cadre, and coded as 0, while lecturer I up to professor are categorize as higher cadre, and coded as 1. Another predictor is number of undergraduate course(s) taught within the period of observation (UGC), and number of postgraduate course(s) taught within the period of observation (UPC).

Descriptive estimates and inferences related to key variables from the two SESTAT surveys when following alternative analytic approaches.

The final dataset and Supplementtary tables regarding to research entitled "Gross motor skills trajectory variation between WEIRD and LMIC countries: A Cross-cultural study" are available.

Sweden Imports of Miscellanneous manufactured articles was US$446.38 Million during 2024, according to the United Nations COMTRADE database on international trade. Sweden Imports of Miscellanneous manufactured articles - data, historical chart and statistics - was last updated on June of 2025.

Numerical issues matter in statistical analysis. Small errors occur when numbers are translated from paper and pencil into the binary world of computers. Surprisingly, these errors may be propagated and magnified through binary calculations, eventually producing statistical estimates far from the truth. In this replication and extension article, we look at one method of verifying the accuracy of statistical estimates by running these same data and models on multiple statistical packages. We find that for two published articles, Nagler (1994, American Journal of Political Science 38:230–255) and Alvarez and Brehm (1995, American Journal of Political Science 39:1055–1089), results are dependent on the statistical package used. In the course of our replications, we uncover other pitfalls that may prevent accurate replication, and make recommendations to ensure the ability for future researchers to replicate results.

Statistical measures and methods in JAMA articles published in 1990, 2000, and 2010*.