Introduction

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

The Kimberley Process (KP) is a system that unites administrations, civil societies and industry in the aim of reducing the flow of conflict diamonds around the world. The Kimberley Process Public Statistics Area is a web page with annual aggregated statistical data relating to the production and trade in rough diamonds.

Website: https://kimberleyprocessstatistics.org/public_statistics

The Annual Statistical Supplement, 2022 includes the most comprehensive data available on the Social Security and Supplemental Security Income programs. More than 250 statistical tables convey a wide range of information about those programs from beneficiary counts and benefit amounts to the status of the trust funds. The tables also contain data on Medicare, Medicaid, veterans' benefits, and other related income security programs. The Supplement also includes summaries of the history of the major programs and of current legislative developments and a glossary of terms used in explaining the programs and data.

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.

The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.

For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

The "Utah 64 Small Health Statistics Areas" feature layer was developed by the Office of Public Health Assessment, Utah Department of Health using small area analysis methodology in 1997. Each feature was generated by combining a sufficient number of adjacent ZIP code area features to form a geographic area of approximately 33,500 persons (range 15,000 to 62,500). Criteria used for determining which ZIP code areas to combine together to form a Small Health Statistics Area included population size, local health district and county boundaries, similarity of ZIP code population's income level and community political boundaries. Input from local community representatives was used to refine area designations. The Utah 64 Small Health Statistics Areas provide a means of geographically analyzing and presenting health statistics at the community level. Producing information at the small area in Utah provides community planners and other with information that is specific to the populations living in their communities of concern. Small area analysis also allows an investigator to explore ecologic relationships between health status, lifestyle, the environment and the health system. In areas where a ZIP code crosses a county boundary, the 2008 and 2009 versions of Small Statistical Areas honor the ZIP code boundary leading to cases where a Small Statistical Areas can be in multiple counties. The 2012 and 2014 versions correct this issue by splitting ZIP code areas by county boundaries resulting in Small Statistical Areas that can only be found in one county. In the 2017 version, area 57 Grand/San Juan Counties was split into 2 areas, area 57.1 Grand county and 57.2 San Juan County.

This dataset collection comprises a set of data tables that are closely connected. These tables are sourced from the 'Tilastokeskus' (Statistics Finland) website based in Finland. All the data tables are structured in a manner that organizes related data in the form of rows and columns, making them easy to read and understand. The dataset is primarily focused on the service interface of the Statistics Finland, providing valuable insights into various statistical parameters. It's important to note that the dataset is updated periodically to reflect the most recent data available. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

BackgroundThe 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.MethodsHere we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.ResultsWe observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.ConclusionsWe have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.

The dataset comprises three dynamic scenes characterized by both simple and complex lighting conditions. The quantity of cameras ranges from 4 to 512, including 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256, and 512. The point clouds are randomly generated.

The market size of the Statistics Software Market is categorized based on Deployment Type (On-Premise, Cloud-Based) and Application (Data Analysis, Data Visualization, Predictive Analytics, Statistical Analysis, Reporting) and End-User Industry (Healthcare, Finance, Retail, Education, Government) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

This dataset is the definitive set of statistical area 2 (SA2) boundaries for 2020 as defined by Stats NZ. This version contains 2,255 SA2 categories.

SA2s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA2018) which replaced the New Zealand Standard Areas Classification (NZSAC1992). The SA2 geography replaces the (NZSAC1992) area unit geography.

SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

SA2s are built from SA1s and either define or aggregate to define urban rural areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In rural areas, many SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

Names are provided with and without tohutō/macrons. The name field without macrons is suffixed ‘ascii’.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal bed resource areas and attributed with statistics on the thickness of the Pond Creek coal zone, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

The monthly Building Approvals (BAPS) collection collects data relating to residential and non-residential building work above certain value limits that have been approved within the reference month. Data from this collection provides timely estimates of future building activity and is an important leading economic indicator. It also provides the sampling framework for the quarterly Building Activity Survey, which is a major contributor to the quarterly National Accounts estimates.

Political scientists have long been interested in citizens' support level for socially sensitive actors such as ethnic minorities, militant groups, and authoritarian regimes. Attempts to use direct questioning in surveys, however, have largely yielded unreliable measures of these attitudes as they are contaminated by social desirability bias and high non-response rates. In this paper, we develop a statistical methodology to analyze endorsement experiments, which recently have been proposed as a possible solution to this measurement problem. The commonly used statistical methods are problematic because they cannot properly combine responses across multiple policy questions, the design feature of a typical endorsement experiment. We overcome this limitation by using item response theory to estimate support levels on the same scale as the ideal points of respondents. We also show how to extend our model to incorporate a hierarchical structure of data in order to recoup the loss of statistical eciency due to indirect questioning. We illustrate the proposed methodology by applying it to measure political support for Islamist milita nt groups in Pakistan. Simulation studies suggest that the proposed Bayesian model yields estimates with reasonable levels of bias and statistical power. Finally, we oer several practical suggestions for improving the design and analysis of endorsement experiments.

Source data assessment of statistical capacity (scale 0 - 100) in Grenada was reported at 60 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Grenada - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Beginning in 2016, the Law Enforcement Management and Administrative Statistics (LEMAS) survey adopted a core and supplement structure. The LEMAS core has been conducted every 3 to 4 years since 1987 with approximately 3,200 local, county and state law enforcement agencies across the United States. Due to the breadth of the survey, detailed analysis of any specific law enforcement topic cannot be done with the LEMAS core. The LEMAS supplements are designed to fill this void by allowing for a more comprehensive examination on a key topic in law enforcement and are administered in between core years. The 2016 LEMAS Body-Worn Camera Supplement (LEMAS-BWCS) is the first supplement administered under the new structure.

Question Paper Solutions of chapter Correlation and Regression Analysis of Numerical and statistical Methods, 5th Semester , Bachelor of Computer Application 2020-2021

This paper exploits an unusual transportation setting to generate some of the first revealed preference value of a statistical life (VSL) estimates from a low-income setting. We estimate the trade-offs individuals are willing to make between mortality risk and cost as they travel to and from the international airport in Sierra Leone. The setting and original dataset allow us to address some typical omitted variable concerns, and also to compare VSL estimates for travelers from different countries, all facing the same choice situation. The average VSL estimate for African travelers in the sample is US$577,000 compared to US$924,000 for non-Africans.