Important statistics, fiscal revenues and expenditures, human resources, unemployment rate, number of employed population, industrial structure of employed individuals, household income and expenditure, consumer price index, land, population, social security, public safety, social welfare and assistance, education and culture, medical care, health, transportation, public construction, industrial and commercial conditions, environmental protection.

This statistic shows the top AI related data priorities in U.S.-based organizations in 2019. Integrating AI and analytics systems was the top AI related data priority for 2019, with ** percent of respondents indicating that it was a top priority of their company.

The most important skills for the development of the enterprise in the coming years by Firm Employment Size, Year and Statistic

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Abstract

Most countries collect official statistics on energy use due to its vital role in the infrastructure, economy and living standards.

In Palestine, additional attention is warranted for energy statistics due to a scarcity of natural resources, the high cost of energy and high population density. These factors demand comprehensive and high quality statistics.

In this contest PCBS decided to conduct a special Energy Consumption in Transport Survey to provide high quality data about energy consumption by type, expenditure on maintenance and insurance for vehicles, and questions on vehicles motor capacity and year of production.

The survey aimed to provide data on energy consumption by transport sector and also on the energy consumption by the type of vehicles and its motor capacity and year of production.

Geographic coverage

Palestine

Analysis unit

Vehicles

Universe

All the operating vehicles in Palestine in 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population: All the operating vehicles in Palestine in 2014.

2.1Sample Frame A list of the number of the operating vehicles in Palestine in 2014, they are broken down by governorates and vehicle types, this list was obtained from Ministry of transport.

2.2.1 Sample size The sample size is 6,974 vehicles.

2.2.2 Sampling Design it is stratified random sample, and in some of the small size strata the quota sample was used to cover them.

The method of reaching the vehicles sample was through : 1-reaching to all the dynamometers (the centers for testing the vehicles) 2-selecting a random sample of vehicles by type of vehicle, model, fuel type and engine capacity

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire was based on the experiences of other similar countries in energy statistics subject to cover the most important indicators for energy statistics in transport sector, taking into account Palestine's particular situation.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The survey questionnaire was uploaded on office computers. At this stage, data were entered into the computer using a data entry template developed in Access Database. The data entry program was prepared to satisfy a number of requirements: ·To prevent the duplication of questionnaires during data entry. ·To apply checks on the integrity and consistency of entered data. ·To handle errors in a user friendly manner. ·The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS. Audit after data entered at this stage is data entered scrutiny by pulling the data entered file periodically and review the data and examination of abnormal values and check consistency between the different questions in the questionnaire, and if there are any errors in the data entered to be the withdrawal of the questionnaire and make sure this data and adjusted, even been getting the final data file that is the final extract data from it. Extraction Results: The extract final results of the report by using the SPSS program, and then display the results through tables to Excel format.

Response rate

80.7%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip).

Data appraisal

The survey sample consisted of around 6,974 vehicles, of which 5,631 vehicles completed the questionnaire, 3,652 vehicles from the West Bank and 1,979 vehicles in Gaza Strip.

During a survey conducted among marketers worldwide in September and October 2024, approximately ** percent included artificial intelligence (AI) skills among the most important skill areas for marketing teams to develop over the following two years. The same share of respondents selected data and analytics skills. According to the same study, *** in ** global marketers expected organizations' marketing budgets to increase between 2024 and 2026.

United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data was reported at 168.237 Jan1997=100 in Nov 2018. This records an increase from the previous number of 166.528 Jan1997=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data is updated monthly, averaging 96.825 Jan1997=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 168.237 Jan1997=100 in Nov 2018 and a record low of 1.998 Jan1997=100 in Jul 1973. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Gross operating surplus and mixed income of non-financial corporations (in EUR) Net lending/net borrowing of non-financial and financial corporations (in EUR) Description copied from catalog.inspire.geoportail.lu.

🇸🇦 사우디아라비아

Key Statistics on Business Performance and Operating Characteristics of the Industrial Sector - Table 610-72005 : Principal statistics for all establishments in the manufacturing sector by industry grouping and number of persons engaged

The Bath and North East Somerset Council has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.

This statistic shows the most important objectives of ethics and compliance (E&C) training to organizations globally in 2018. During the survey, ** percent of respondents stated that creating a culture of ethics and respect was an important objective of E&C training, making this the most widely-held objective.

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 1997; the enumeration area consists of buildings and housing units and is composed of an average of 120 households. The enumeration areas were used as Primary Sampling Units (PSUs) in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 299 enumeration areas. Second stage: selection of a systematic random sample of 12-18 households from each enumeration area selected in the first stage. A person (18 years and more) was selected from each household in the second stage.

Sample strata:

The population was divided by: 1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Sample Size:

The calculated sample size is 3,781 households.

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Detailed information/formulas on the sampling design are available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month,and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e, Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Both data entry and tabulation were performed using the ACCESS and SPSS software programs. The data entry process was organized in 6 files, corresponding to the main parts of the questionnaire. A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consistency checks and cross-validation. Complete manual inspection was made of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 3,781 households interviewed over a twelve-month period between January 2004 and January 2005. There were 3,098 households that completed the interview, of which 2,060 were in the West Bank and 1,038 households were in GazaStrip. The response rate was 82% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimations enable the user to identify the accuracy of estimations and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting all various related activities. The work team spared no effort at different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the “programming package” CENVAR.

This statistic shows the main import partners of Azerbaijan in 2024. In 2024, the most important import partners for Azerbaijan was China with a share of 18 percent in all imports.

The British Geological Survey has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.

Key Statistics on Business Performance and Operating Characteristics of the Accommodation and Food Services Sector - Table 625-76102 : Principal Statistics for All Establishments by Industry Grouping and Number of Persons Engaged (Accommodation and Food Services Sector)

Nantou County environmental information, including air, waste, environmental sanitation and toxic substance management, public hazard petitions, and other relevant statistics in five major categories of data

This table contains information on the finances of the general government sector. The terms and definitions used are in accordance with the framework of the National Accounts. The National Accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date. Data available from: Yearly figures from 1995, quarterly figures from 1999. Status of the figures: The figures for the period 1995-2020 are final. The quarterly figures for 2021 are provisional. The annual figures for 2021 are final. The figures for 2022 and 2023 are provisional. Changes as of 22 September 2023: Figures for the second quarter of 2023 are available. The figures for 2021 and 2022 have been revised. Revenue and expenditure of the State and social security funds in 2021 have been revised due to updated information. Revenue of the State in 2022 has also been revised due to updated information. The national accounts and government finance statistics have recorded these revisions differently in 2021. The national accounts will be aligned with government finance statistics on 24 June 2024. (Seasonal adjusted) government revenue, (seasonal adjusted) government expenditure and (seasonal adjusted) balance of general government have been adjusted. When will new figures be published? Initial quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter are revised, in December the figures on the second quarter are revised and in March the first three quarters are revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to revised years at the end of June. More information on the revision policy of National Accounts can be found under 'relevant articles' under paragraph 3.

The environmental status data of Yilan County includes five major categories of data such as air, waste, environmental hygiene and toxic substance management, pollution complaints, and other relevant statistics.

Developers using the DOL-wide API have access to a variety of queries providing usage metrics for their app's key.

Background Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different conditions. Inferences about the biological relevance of observed changes in expression depend on the statistical significance of the changes. In lieu of many replicates with which to determine accurate intensity means and variances, reliable estimates of statistical significance remain problematic. Without such estimates, overly conservative choices for significance must be enforced. Results A simple statistical method for estimating variances from microarray control data which does not require multiple replicates is presented. Comparison of datasets from two commercial entities using this difference-averaging method demonstrates that the standard deviation of the signal scales at a level intermediate between the signal intensity and its square root. Application of the method to a dataset related to the β-catenin pathway yields a larger number of biologically reasonable genes whose expression is altered than the ratio method. Conclusions The difference-averaging method enables determination of variances as a function of signal intensities by averaging over the entire dataset. The method also provides a platform-independent view of important statistical properties of microarray data.