This statistic shows the share of people using the airplane as a means of transport in France in 2015, by frequency. In 2015, an estimated 40 percent of respondents never took the plane while approximately 30 percent traveled by plane less than once a year.

The Market Saturation and Utilization Core-Based Statistical Areas (CBSA) dataset provides monitoring of market saturation as a means to help prevent potential fraud, waste, and abuse (FWA). CBSAs are geographical delineations that are Census Bureau-defined urban clusters of at least 10,000 people. Market saturation, in the present context, refers to the density of providers of a particular service within a defined geographic area relative to the number of beneficiaries receiving that service in the area. The data can be used to reveal the degree to which use of a service is related to the number of providers servicing a geographic region. There are also a number of secondary research uses for these data, but one objective of making these data public is to assist health care providers in making informed decisions about their service locations and the beneficiary population they serve. The interactive dataset can be filtered and analyzed on the site or downloaded in Excel format.

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.

In September 2020, the share of respondents in Japan willing to take trains decreased from about ** to ** percent depending on whether they feared a COVID-19 infection or not. Contrary to means of public transportation, personal vehicles hardly suffered any loss of trust. The research institute further investigated the respondents' willingness to rent such kind of vehicles.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Analysis of ‘Youth statistics: Daily use of the bicycle as a means of transport’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-opendata-euskadi-eus-catalogo-estadisticas-sobre-juventud-uso-diario-de-la-bicicleta-como-medio-de-transporte- on 18 January 2022.

--- Dataset description provided by original source is as follows ---

The Basque Youth Observatory is an instrument of the Basque Government that provides a comprehensive and permanent overview of the situation and evolution of the youth world, allowing an assessment of the impact of the actions carried out in the CAPV by the different administrations in the field of youth.The Basque Youth Observatory regularly publishes more than 100 statistical indicators that can be consulted in euskadi.eus, together with other research and reports. Statistics are provided in various formats (csv, excel).

--- Original source retains full ownership of the source dataset ---

Global ad spend were expected to reach over $134 billion in 2022. This means that it has increased by over 17% yearly.

This statistic shows how French consumers defined "responsible consumption" in 2017. According to this survey, for more than ** percent of respondents, "responsible consumption" meant to buy ecological products or local products, among others.

Introduction

Digital Footprint Statistics: A digital footprint is the trail of information people leave behind when using the internet. It includes everything from social media posts to online searches, websites visited, and emails sent. Some of this data is shared intentionally, like posting on Facebook, while other parts are collected automatically, like tracking cookies from websites.

A digital footprint can be active, meaning data is shared by choice, or passive, meaning it is collected without you realizing it. It's important to manage your digital footprint because it can affect your privacy, reputation, and even job opportunities in the future. Understanding it helps you stay safe online.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The Basque Youth Observatory is an instrument of the Basque Government that allows to have a global and permanent vision of the situation and evolution of the youth world that allows to evaluate the impact of the actions carried out in the CAPV by the different administrations in the field of youth.The Basque Youth Observatory regularly publishes more than 100 statistical indicators that can be consulted in euskadi.eus, along with other research and reports. Statistics are provided in various formats (csv, excel).

In this article we reply to Silva and Guarnieri (2014) comments on Figueiredo Filho et al (2013) paper published by the Brazilian Political Science Review. Originally, we developed four recommendations: (1) scholars must always graphically analyze their data before interpreting the p-value; (2) it is pointless to estimate the p-value for non-random samples; (3) the p-value is highly affected by the sample size and (4) it is pointless to estimate the p-value when dealing with data on population. Here we defend our view about the proper use the p-value statistic and we use both observational and simulation data to make our case. Substantively, we hope to advance the debate about statistical significance in Political Science.

Snapchat now boasts over 319 million daily active users. That means it’s one of the most engaging platforms. Snapchat currently has a total user base of 800 million.

Abstract

Time Use Surveys (TUS) are household-based surveys that measure and analyze time spent by women and men, girls and boys on different activities over a specified period. Unlike data from other surveys, time use results can be specific and comprehensive in revealing the details of a person's daily life. The results of the Time Use Survey enable one to identify what activities are performed, how they are performed and how long it takes to perform such activities. The Department of Census and Statistics (DCS) conducted the first Sri Lanka national survey on time use statistics in 2017. The primary objective of TUS is to measure the participation of men and women in paid and unpaid activities. Moreover, this report contains information on the time spent on unpaid care giving activities, voluntary work, and domestic service of the household members. This also provides information on time spent on learning, socializing, leisure activities and self-care activities of 10 years and above aged Sri Lankans. In this report, statistics were estimated under following three indicators. 1. Participation rate 2. The mean actor time spent on different activities 3. The mean population time spent on different activities

The TUS was conducted in the same households of the fourth quarter Labour Force Survey (LFS) sample in 2017. It was non-independent survey but administered an independent diary and a household module with fourth quarter LFS, 2017. All household members who were age 10 years and above in the sample were provided a diary to record activities done in every 15 minutes within a period of 24 hours (day). The TUS sample covered the household population aged 10 years and above - thus representing an estimated 17.87 million people. Classification of activities Reported activities were coded according to the International Classification of Activities for Time Use Statistics (ICATUS 2016). The ICATUS 2016 has nine broad categories, which aggregate into even broader categories. The categories are consistent with the System of National Accounts (SNA) which underlies the calculation of gross domestic product (GDP). The categories are as follows: 1. Employment and related activities 2. Production of goods for own final use 3. Unpaid domestic services for household and family members 4. Unpaid caregiving services for household and family members 5. Unpaid volunteer, trainee and other unpaid work 6. Learning 7.Socializing and communication, community participation and religious practice 8. Culture, leisure, mass-media and sports practices 9. Self-care and maintenance Activity category number 1 and 2 falls in to SNA production boundary. Therefore, most part be 'counted' in national accounts and the GDP. Activity categories 3 to 5, which cover unpaid household work and unpaid assistance to other households, fall outside the SNA production boundary, although they are recognized as 'productive'. They correspond to what is commonly referred to as unpaid care work. The remaining four activity categories cannot be performed for a person by someone else; people cannot hire someone else to sleep, learn, or eat for them. Hence, they do not qualify as' work 'or' production' in terms of the third-person 'rule'.

Geographic coverage

The survey collects data from a quarterly sample of 6,440 housing units covering the whole country, also this sample enough to provides national estimates on Time use statistics. It covers persons living in housing units and excludes the institutional population.

Analysis unit

Individual,Household

Universe

All household members who were age 10 years and above

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame prepared for 2012 Census of Population and Housing (CPH) is used as sample frame for the sample selection of LFS in 2017. Two stage stratified sampling procedure is adopted to Sri Lanka Time Use Survey Final Report - 2017 1.5 Field Work Select the annual LFS sample of 25,750 housing units. 2,575 Primary Sampling Units (PSU?s) were allocated to each district and to each sector (Urban, Rural and Estate) and equally distributed for 12 months. Housing units are the Secondary Sample Units (SSU). From each selected PSU, 10 housing units (SSU) are selected for the survey using systematic random sampling method. Since, the Time Use survey was planned to disseminate statistics at national level, a quarterly sample of 6,440 housing units of the LFS 4th quarter 2017 sample was selected for the TUS. Also, selected housing units of a PSU were evenly allocated to cover all 7 days of a week including weekends. Sample allocation by sector for TUS - 2017

Number of housing units 

Sri Lanka 6,440
Urban 1,000
Rural 5,140
Estate 300

Mode of data collection

Face-to-face [f2f]

Research instrument

The Survey was conducted in the same households of the fourth quarter Labour Force Survey (LFS) sample in 2017. It was non-independent survey but consists with other two data collection instruments in PAPI method: a) A household questionnaire b) A time diary with fourth quarter LFS 2017 questionnaire in CAPI method. The household questionnaire was designed only for obtain information on the characteristics of the household. Because the LFS questionnaire collects background information about the demographic and socio-economic characteristics of the respondent, such as their labour force status. All household members who were age 10 years and above in the sample were provided a diary to record activities done in every 15 minutes within a period of 24 hours (day). It captures information on spending the time for main activity, simultaneous activity, where the activity takes place and with whom the activity takes place.

Cleaning operations

The International Classification of Activities for Time Use Statistics (ICATUS 2016) has been developed based on internationally agreed concepts, definitions and principles in order to improve the consistency and international comparability of time use and other social and economic statistics. Reliable time use statistics have been critical for

(a) the measurement and analysis of quality of life or general well-being; (b) a more comprehensive measurement of all forms of work, including unpaid work and non-market production and the development of household production accounts; and (c) producing data for gender analysis for public policies. Hence, the importance of ICATUS link and consistency with the System of National Accounts (SNA) and the International Conference of Labour Statisticians (ICLS) definition and framework for statistics of work. Additionally, ICATUS will serve as an important input for monitoring progress made towards the achievement of the Sustainable Development Goals (SDGs). ICATUS 2016 is a three-level hierarchical classification (composed of major divisions, divisions, and groups) of all possible activities undertaken by the general population during the 24 hours in a day. 1) The first level, one-digit code or "major division" represents the least detailed level or the broadest group of activities. 2) The second level, two-digit code or "division" represents more detailed activities than the preceding one 3) The third level, three-digit code or "group" is considered the most detailed level of the classification detailing specific activities. The purpose of the classification is to provide a framework that can be used to produce meaningful and comparable statistics on time use across countries and over time.

An important aspect of the UN classification system is the fact that it matches the System of National Accounts (SNA), which forms the basis internationally for calculating gross domestic product (GDP). The classification is organized according to nine broad activity categories. These categories can be distinguished by the first digit of the three-digit activity code The nine broad categories are as follows: SNA Production Activities 1. Employment and related activities 2. Production of goods for own final use

Non -SNA Production Activities 3. Unpaid domestic services for household and family members 4. Unpaid caregiving services for household and family members 5. Unpaid volunteer, trainee and other unpaid work

Non-Productive Activities 6. Learning 7. Socializing and communication, community participation and religious practice 8. Culture, leisure, mass-media and sports practices 9. Self-care and maintenance

Activity categories 1-2, which are the two 'work' divisions referred to above, fall in the SNA production boundary. They would thus be 'counted' in national accounts and the GDP. The only exceptions are the codes for looking for work, and time spent on travelling related to SNA-type activity. Activity categories 3-5, which cover unpaid household work and care work for household and family members and assistance to other households, fall outside the SNA general production boundary, although they are recognized as 'productive'. In this report they are referred to as non-SNA production Activities. The remaining activity categories are not covered by the SNA. These activities cannot be performed for a person by someone else - people cannot hire someone else to sleep, learn, or eat for them. They thus do not qualify as'work 'or 'production' terms of the „third-person rule. In this report they are referred to as non-productive activities. Many of the tables in the report are organized according to either the nine categories, or the three SNA-related groupings of these categories.

Response rate

Please refer page number 11 and 12 of annual

MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory -------------------------------------------------------------------------------------- MSZSI is a data extraction tool for Google Earth Engine that aggregates time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. The code at the bottom of this page (metadata) can be pasted into the Google Earth Engine JavaScript code editor and ran at https://code.earthengine.google.com/. Input options: [1] Country of interest [2] Start and end year [3] Start and end month [4] Option to mask data to a specific land-use/land-cover type [5] Land-use/land-cover type code from CGLS LULC [6] Image collection for data aggregation [7] Desired band from the image collection [8] Statistics type for the zonal aggregations [9] Statistic to use for annual aggregation [10] Scaling options [11] Export folder and label suffix Output: Two CSVs containing zonal statistics for each of the FAO GAUL administrative level boundaries Output fields: system:index, 0-ADM0_CODE, 0-ADM0_NAME, 0-ADM1_CODE, 0-ADM1_NAME, 0-ADMN_CODE, 0-ADMN_NAME, 1-AREA_PERCENT_LULC, 1-AREA_SQM_LULC, 1-AREA_SQM_ZONE, 2-X_2001, 2-X_2002, 2-X_2003, ..., 2-X_2020, .geo PREPROCESSED DATA DOWNLOAD The datasets available for download contain zonal statistics at 2 administrative levels (FAO GAUL levels 1 and 2). Select countries from Southeast Asia and Sub-Saharan Africa (Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Thailand, Vietnam, Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe) are included in the current version, with plans to extend the dataset to contain global metrics. Each zip file is described below and two example NDVI tables are available for preview. Key: [source, data, units, temporal range, aggregation, masking, zonal statistic, notes] Currently available: MSZSI-V2_V-NDVI-MEAN.tar: [NASA-MODIS, NDVI, index, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-DAY-MEAN.tar: [NASA-MODIS, LST Day, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-NIGHT-MEAN.tar: [NASA-MODIS, LST Night, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_R-PRECIP-SUM.tar: [UCSB-CHG-CHIRPS, Precipitation, mm, 2001–2020, annual sum, agriculture, mean, n/a] MSZSI-V2_S-BDENS-MEAN.tar: [OpenLandMap, Bulk density, g/cm3, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-ORGC-MEAN.tar: [OpenLandMap, Organic carbon, g/kg, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-PH-MEAN.tar: [OpenLandMap, pH in H2O, pH, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-WATER-MEAN.tar: [OpenLandMap, Soil water, % at 33kPa, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SAND-MEAN.tar: [OpenLandMap, Sand, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SILT-MEAN.tar: [OpenLandMap, Silt, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-CLAY-MEAN.tar: [OpenLandMap, Clay, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_E-ELEV-MEAN.tar: [MERIT, [elevation, slope, flowacc, HAND], [m, degrees, km2, m], static, n/a, agriculture, mean, n/a] Coming soon MSZSI-V2_C-STAX-MEAN.tar: [OpenLandMap, Soil taxonomy, category, static, n/a, agriculture, area sum, n/a] MSZSI-V2_C-LULC-MEAN.tar: [CGLS-LC100-V3, LULC, category, 2015–2019, mode, none, area sum, n/a] Data sources: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1 https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01 https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1 https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0 https://developers.google.com/earth-engine/datasets/catalo... Visit https://dataone.org/datasets/sha256%3A1844d916f64551cf0a8e0fe8d71474912d22e43d77c43c848aa8fac7e7e02f29 for complete metadata about this dataset.

Statistical Mean for Apparent Oxygen Utilization (AOU) World Ocean Atlas measured via In-Situ in null. Part of dataset World Ocean Atlas 2013 v2 Climatology

Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative test statistics exist that each come with different versions, strengths and weaknesses. This paper gives an overview about these statistics, including the Wald, the LRT, the Score, the F¯- and the D-statistic. Simulation studies are presented that clarify their performance in terms of type I and type II error rates under different conditions. Based on the results, it is recommended to use the Wald and F¯-test rather than the LRT and Score test as the former need less computing time. Furthermore, it is favorable to use the degrees of freedom corrected rather than the naive mean squared error when calculating the test statistics as well as using the F¯- rather than the χ¯2-distribution when calculating the p-values.

The population share with internet access in the United States was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the ninth consecutive increasing year, the internet penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the population share with internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via any means. The shown figures have been derived from survey data that has been processed to estimate missing demographics. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: https://doi.org/10.1016/j.jss.2019.07.002, preprint: https://arxiv.org/abs/1706.00933).

The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.

The extraction tool mentioned in the paper is available in dockerhub via: https://hub.docker.com/r/robertfeldt/sept