Financial overview and grant giving statistics of Php Foundation Inc

Comprehensive YouTube channel statistics for PHP Funny Game, featuring 154,000 subscribers and 63,004 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Gaming category and is based in US. Track 10 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Financial overview and grant giving statistics of P H P Inc.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the PHP tech stack in 2024 was CodeIgniter, chosen by over ** percent of respondents. WordPress ranked second, being preferred by more than ** percent of respondents.

The PHP Web Frameworks Software market has experienced significant evolution over the years, solidifying its position as a crucial component in web development. With PHP being one of the most popular server-side scripting languages, various frameworks have emerged to enhance its capabilities, offering developers str

The statistics of datasets.

Financial overview and grant giving statistics of Community Development Corporation of Php Nfp

Collection of college statistics, draft team information, and NFL career statistics for every quarterback drafted since the year 2000 until the 2024 offseason. Originally created in an attempt to train a neural network that predicts NFL success level of a quarterback at the time of being drafted.

This database was only made possible by the many NFL stat keeping websites I discovered in the data collection process:

• Draft Data: https://www.drafthistory.com/index.php/positions/qb
• College Statistics: https://www.sports-reference.com/cfb
• Physical Combine Measurements: https://www.pro-football-reference.com
• Assorted Missing Stats: https://www.walterfootball.com
• NCAA Conference Strength Metric: https://www.jhowell.net/cf/
• Assorted Missing QB Stats: https://www.ncaa.com and respective school websites
• NFL Team Records: https://www.jt-sw.com/football/
• Coach Tenure: https://www.pro-football-history.com/
• NFL Stats: https://www.stathead.com/ (paid subscription)

Database Feature Glossary

year-drafted: The year drafted into the NFL

qb-num-picked: The number taken relative to other quarterbacks (1 = first quarterback selected, 2 = second selected, etc.)

rd-picked: The round of the NFL draft the player was selected

num-picked: The overall draft position the player was drafted at

name: Name of player

height (in): Player height in inches as reported at the NFL Draft

weight (lbs): Player weight in pounds as reported at the NFL Draft

nfl-team: The NFL team that drafted the player

coach-tenure: The number of years the head coach had been employed by the team that drafted the player at the time of the draft

drafted-team-winpr: The win percentage in the most recent season of the team that drafted the player at the time of drafting

drafted_team_ppg_rk: The points per game ranking in the most recent season of the team that drafted the player at the time of drafting

college: The college the player attended at the time of drafting

conf: The conference of the college the player participated in

conf-str: The calculated strength of the conference in the final year the quarterback played (reference link above)

p-cmp: Pass completions in college career

p-att: Pass attempts in college career

cmp-pct: Pass completion percentage in college career

p-yds: Total pass yards in college career

p-ypa: Passing yards per attempt in college career

p-adj-ypa: Adjusted passing yards per attempt in college career

p-td: Passing touchdowns in college career

int: Interceptions in college career

rate: Passing efficiency rating (reference link above)

r-att: Rushing attempt count in college career

r-yds: Rushing yards in college career

r-avg: Average yards per rush in college career

r-tds: Rushing touchdowns in college career

nfl-starts: Total number of started games in the NFL

nfl-wins: Total games won in the NFL

nfl-losses: Total games lost in the NFL

nfl-ties: Total games tied in the NFL

nfl-winpr: Total win percentage as a starter in the NFL

nfl-qbr: Quarterback rating in the NFL

nfl-cmp: Total pass completions in the NFL

nfl-att: Total pass attempts in the NFL

nfl-inc: Total incompletions thrown in the NFL

nfl-comp%: Career completion percentage in the NFL

nfl-yds: Total passing yards in the NFL

nfl-tds: Total passing touchdowns in the NFL

nfl-int: Total interceptions thrown in the NFL

nfl-pick6: Number of interceptions thrown that were returned for touchdowns in the NFL

nfl-int%: Percentage of NFL throws that were interceptions

nfl-sack%: Percentage of NFL passing plays the player gave up a sack

nfl-y/a: Yards per passing attempt in the NFL

nfl-ay/a: Adjusted yards per passing attempt in the NFL

nfl-any/a: Adjusted net yards per passing attempt in the NFL

nfl-y/c: Passing yards per completion in the NFL

nfl-y/g: Passing yards per game in the NFL

nfl-succ%: Passing success rate in the NFL (reference link above)

nfl-4qc: 4th quarter comebacks completed in the NFL

nfl-gwd: Game winning drives completed in the NFL

Take up of Recruitment Subsidy. Self Employment Work Focused Training and Volunteering initiatives introduced in April 2009 as part of the Government's response to the economic downturn. https://webarchive.nationalarchives.gov.uk/+/http://statistics.dwp.gov.uk/asd/index.php?page=6month_offer https://www.gov.uk/government/collections/jobseekers-allowance-statistics-on-the-six-month-offer--2 Source agency: Work and Pensions Designation: Experimental Official Statistics Language: English Alternative title: SMO

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

The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU, the United Kingdom, EFTA and Candidate countries.

The EU-LFS survey follows the definitions and recommendations of the http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:International_Labour_Organization_(ILO)">International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU-LFS (Statistics Explained) webpage.

The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level.

At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However, many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation by territorial typologies, i.e. urban-rural, metropolitan, coastal, mountain, borders and island typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.

Financial overview and grant giving statistics of Explorations Php Inc.

Abstract

Vital Statistics cover Births, Deaths, Still births and Marriages which are called vital events. The source for the collection of data for the preparation of Vital statistics is the certificate issued to the respondent when the registration of the occurrence of the vital event is done. Maintaining Vital statistics is an Administrative record keeping operation and is a continuous process where the event by event data are collected on a monthly basis and the final outputs (reports) are produced annually for dissemination. The computerization of vital statistics came into being after the arrival of computers to the Department of Census and Statistics in 1960's.

Registration of vital events commenced in 1867 with the enactment of civil registration laws which conferred the legal sanction for the registration of events namely, live births, deaths, still births and marriages.

Geographic coverage

National coverage.

Analysis unit

Each marriage registered within the month

Universe

Marriages and divorces recorded by the representatives of the Registrar Generals Office.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU, the United Kingdom, EFTA and Candidate countries.

The EU-LFS survey follows the definitions and recommendations of the http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:International_Labour_Organization_(ILO)">International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU-LFS (Statistics Explained) webpage.

The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level.

At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However, many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation by territorial typologies, i.e. urban-rural, metropolitan, coastal, mountain, borders and island typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.

Philippines PT: Value: TP: Automated Clearing Houses (AC) data was reported at 165,122.362 PHP mn in Jul 2020. This records an increase from the previous number of 119,756.772 PHP mn for Mar 2020. Philippines PT: Value: TP: Automated Clearing Houses (AC) data is updated monthly, averaging 117,483.506 PHP mn from Jan 2020 (Median) to Jul 2020, with 4 observations. The data reached an all-time high of 165,122.362 PHP mn in Jul 2020 and a record low of 109,925.319 PHP mn in Feb 2020. Philippines PT: Value: TP: Automated Clearing Houses (AC) data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KA010: Payment System Statistics.

The source for the regional labour market information down to NUTS level 2 is the EU Labour Force Survey (EU-LFS). This is a quarterly household sample survey conducted in all Member States of the EU, the United Kingdom, EFTA and Candidate countries.

The EU-LFS survey follows the definitions and recommendations of the http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:International_Labour_Organization_(ILO)">International Labour Organisation (ILO). To achieve further harmonisation, the Member States also adhere to common principles when formulating questionnaires. The LFS' target population is made up of all persons in private households aged 15 and over. For more information see the EU-LFS (Statistics Explained) webpage.

The EU-LFS is designed to give accurate quarterly information at national level as well as annual information at NUTS 2 regional level and the compilation of these figures is well specified in the regulation. Microdata including the NUTS 2 level codes are provided by all the participating countries with a good degree of geographical comparability, which allows the production and dissemination of a complete set of comparable indicators for this territorial level.

At present the transmission of the regional labour market data at NUTS 3 level has no legal basis. However, many countries transmit NUTS 3 figures to Eurostat on a voluntary basis, under the understanding that they are not for publication with such detail, but for aggregation by territorial typologies, i.e. urban-rural, metropolitan, coastal, mountain, borders and island typology. Most of the NUTS 3 data are based on the LFS while some countries transmit data based on registers, administrative data, small area estimation and other reliable sources.

Abstract

In researching ICT penetration rates of a country, it is necessary to look at the target population demographic characteristics that facilitate use, access and ownership of the ICT facilities and equipments. As such, the ICT survey sought information on the general characteristics of the sampled population, including composition by age and sex, household size, education, employment, literacy, disability and source of electricity to households.

Geographic coverage

Kenya

Analysis unit

District

Universe

Kenya

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified Sample methodology

Mode of data collection

Face-to-face [f2f]

Cleaning operations

As a matter of procedure initial manual editing was done in the field by the RAs. The supervisors further checked the questionnaires and validated the data in the field by randomly sampling 20 per cent of the filled questionnaires. After the questionnaires were received from the field, an office editing team was constituted to do office editing.

Data was captured using Census and Survey Processing System (CSPRO) version 4.0 through a data entry screen specially created with checks to ensure accuracy during data entry. All questionnaires were double entered to ensure data quality. Erroneous entries and potential outliers were then verified and corrected appropriately. A total of 20 data entry personnel were engaged during the exercise.

The captured data were exported to Statistical Package for Social Sciences (SPSS) for cleaning and analysis. The cleaned data was weighted before final analysis. The weighting of the data involved application of inflation factors derived from the selection probabilities of the EAs and households detailed in section 2.2.7, on weighting the Sample Data.

Response rate

Owing to the some logistical challenges the following clusters were partially or not covered at all: • One cluster in Tana River due to floods. • Two clusters in Molo where households shifted to safer areas after the Post Election Violence (PEV). As a result, fewer than the expected households were covered. • One cluster in Koibatek was covered halfway due to relocation of households to pave way for a large plantation.