Since the beginning of the 1960s, Statistics Sweden, in collaboration with various research institutions, has carried out follow-up surveys in the school system. These surveys have taken place within the framework of the IS project (Individual Statistics Project) at the University of Gothenburg and the UGU project (Evaluation through follow-up of students) at the University of Teacher Education in Stockholm, which since 1990 have been merged into a research project called 'Evaluation through Follow-up'. The follow-up surveys are part of the central evaluation of the school and are based on large nationally representative samples from different cohorts of students.

Evaluation through follow-up (UGU) is one of the country's largest research databases in the field of education. UGU is part of the central evaluation of the school and is based on large nationally representative samples from different cohorts of students. The longitudinal database contains information on nationally representative samples of school pupils from ten cohorts, born between 1948 and 2004. The sampling process was based on the student's birthday for the first two and on the school class for the other cohorts.

For each cohort, data of mainly two types are collected. School administrative data is collected annually by Statistics Sweden during the time that pupils are in the general school system (primary and secondary school), for most cohorts starting in compulsory school year 3. This information is provided by the school offices and, among other things, includes characteristics of school, class, special support, study choices and grades. Information obtained has varied somewhat, e.g. due to changes in curricula. A more detailed description of this data collection can be found in reports published by Statistics Sweden and linked to datasets for each cohort.

Survey data from the pupils is collected for the first time in compulsory school year 6 (for most cohorts). Questionnaire in survey in year 6 includes questions related to self-perception and interest in learning, attitudes to school, hobbies, school motivation and future plans. For some cohorts, questionnaire data are also collected in year 3 and year 9 in compulsory school and in upper secondary school.

Furthermore, results from various intelligence tests and standartized knowledge tests are included in the data collection year 6. The intelligence tests have been identical for all cohorts (except cohort born in 1987 from which questionnaire data were first collected in year 9). The intelligence test consists of a verbal, a spatial and an inductive test, each containing 40 tasks and specially designed for the UGU project. The verbal test is a vocabulary test of the opposite type. The spatial test is a so-called ‘sheet metal folding test’ and the inductive test are made up of series of numbers. The reliability of the test, intercorrelations and connection with school grades are reported by Svensson (1971).

For the first three cohorts (1948, 1953 and 1967), the standartized knowledge tests in year 6 consist of the standard tests in Swedish, mathematics and English that up to and including the beginning of the 1980s were offered to all pupils in compulsory school year 6. For the cohort 1972, specially prepared tests in reading and mathematics were used. The test in reading consists of 27 tasks and aimed to identify students with reading difficulties. The mathematics test, which was also offered for the fifth cohort, (1977) includes 19 assignments. After a changed version of the test, caused by the previously used test being judged to be somewhat too simple, has been used for the cohort born in 1982. Results on the mathematics test are not available for the 1987 cohort. The mathematics test was not offered to the students in the cohort in 1992, as the test did not seem to fully correspond with current curriculum intentions in mathematics. For further information, see the description of the dataset for each cohort.

For several of the samples, questionnaires were also collected from the students 'parents and teachers in year 6. The teacher questionnaire contains questions about the teacher, class size and composition, the teacher's assessments of the class' knowledge level, etc., school resources, working methods and parental involvement and questions about the existence of evaluations. The questionnaire for the guardians includes questions about the child's upbringing conditions, ambitions and wishes regarding the child's education, views on the school's objectives and the parents' own educational and professional situation.

The students are followed up even after they have left primary school. Among other things, data collection is done during the time they are in high school. Then school administrative data such as e.g. choice of upper secondary school line / program and grades after completing studies. For some of the cohorts, in addition to school administrative data, questionnaire data were also collected from the students.

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Year 6 age children (three year average between academic years 2016/17, 2017/18, 2018/19).Although this layer is symbolised based on an overall score for excess weight, the underlying data, including the raw data for Year 6 children, is included in the dataset.ANALYSIS METHODOLOGYEach MSOA was given a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the NUMBER of Year 6 children with excess weight and;B) the PERCENTAGE of Year 6 children with excess weight.An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of Year children with excess weight, compared to other MSOAs. In other words, those are areas where a large number of children have excess weight, and where those children make up a large percentage of the population of that age group, suggesting there is a real issue with childhood obesity in that area that needs addressing.DATA SOURCESNational Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEBased on data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. Data analysed and published by Ribble Rivers Trust © 2021.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The municipality of Umeå is divided into key code areas, "nyckelkodsområden", The finest division of key code areas is the 6-digit divisionThis division is primarily made to facilitate the planning of various municipal activities based on the population distribution across different parts of the municipality.Please note that the map boundaries are only indicative.The dataset is typically updated once a year or as needed.For questions, contact analysgruppen@umea.se.

IT: Life Expectancy at Birth: Male data was reported at 80.300 Year in 2016. This stayed constant from the previous number of 80.300 Year for 2015. IT: Life Expectancy at Birth: Male data is updated yearly, averaging 73.200 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 80.700 Year in 2014 and a record low of 66.540 Year in 1962. IT: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Question Paper Solutions of chapter Descriptive Statistics of Probability and Statistics, 2nd Semester , Master of Computer Applications (2 Years)

This data for 2019 shows that most paid users who subscribed for high school EdTech services did so after ** to ** days of trial period in India, with ** percent among grade six to eight students and ** percent among grade nine to twelve students. Almost another third of the paid users had already subscribed within ** days of their trial. Only a small percentage of the paid users had subscribed without testing or with a longer trial than a month.

Data Notes:\r \r * The data only includes students learning a language on average for more than 1 hour per week for 35 or more weeks a year.\r \r * Includes students studying a language through the Secondary College of Languages (formerly Saturday School of Community Languages).\r \r * In 2021, the Language Participation Collection for Years 7-9 students was moved from August to May.\r \r * Programs in Languages other than English for Years K-6 and the Language Participation for Years 7-9 data collections were not conducted in 2022, in line with the department’s commitment to “clear the decks” for schools in Term 2 2022.\r \r Data Source:\r \r * Schools and Students: Statistical Bulletin . Centre for Education Statistics and Evaluation.\r \r

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Brazil Population: Non Literate: South: 5 Years to 9 Years: 6 Years data was reported at 136,005.000 Person in 2010. This records a decrease from the previous number of 299,951.000 Person for 2000. Brazil Population: Non Literate: South: 5 Years to 9 Years: 6 Years data is updated yearly, averaging 299,951.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 365,458.000 Person in 1991 and a record low of 136,005.000 Person in 2010. Brazil Population: Non Literate: South: 5 Years to 9 Years: 6 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD058: Population: Non Literate: by Region.

About this dataset

Context

The dataset tabulates the population of Goodnews Bay by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Goodnews Bay. The dataset can be utilized to understand the population distribution of Goodnews Bay by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Goodnews Bay. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Goodnews Bay.

Key observations

Largest age group (population): Male # 0-4 years (6) | Female # 0-4 years (6). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Goodnews Bay population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Goodnews Bay is shown in the following column.
• Population (Female): The female population in the Goodnews Bay is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Goodnews Bay for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Goodnews Bay Population by Gender. You can refer the same here

The 0-6 Years Early Learning Services market plays a crucial role in providing foundational education and developmental support to children at a critical stage of their growth. This market encompasses a wide range of services, including preschool programs, daycare facilities, and early intervention initiatives, all

The Survey of Health, Ageing and Retirement in Europe (SHARE), is a longitudinal micro-data infrastructure created in response to a communication by the European Commission (2000) to the Council and the European Parliament, which identified population ageing and its social and economic challenges to growth and prosperity to be among the most pressing challenges of the 21st century in Europe. SHARE has also become one of the most prestigious social science infrastructures and was in 2011 the first to be appointed a European Research Infrastructure Consortium (ERIC) by the European Council.The overarching objective of SHARE is to better understand the interactions between bio-medical factors, the socio-economic environment and policy interventions in the ageing European populations. SHARE aims to achieve this objective by providing a research infrastructure for fundamental science as well as a tool for policy evaluation and design. Initiated in 2002, SHARE is scheduled to launch, all in all, 10 data collection waves. At present eight waves have been fulfilled and seven waves are available to the research community.

Please also cite the following publications in addition to the SHARE acknowledgement:

Malter, F. and A. Börsch-Supan (Eds.) (2017). SHARE Wave 6: Panel innovations and collecting Dried Blood Spots. Munich: Munich Center for the Economics of Aging (MEA). Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S. and Zuber, S. (2013). Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology DOI: 10.1093/ije/dyt088.

Brazil Population: Non Literate: North: Pará: 5 Years to 9 Years: 6 Years data was reported at 96,406.000 Person in 2010. This records a decrease from the previous number of 127,489.000 Person for 2000. Brazil Population: Non Literate: North: Pará: 5 Years to 9 Years: 6 Years data is updated yearly, averaging 127,489.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 128,436.000 Person in 1991 and a record low of 96,406.000 Person in 2010. Brazil Population: Non Literate: North: Pará: 5 Years to 9 Years: 6 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD065: Population: Non Literate: by Municipality: North: Pará.

Brazil Population: Non Literate: North: Pará: Belem: 5 Years to 9 Years: 6 Years data was reported at 8,841.000 Person in 2010. This records a decrease from the previous number of 16,807.000 Person for 2000. Brazil Population: Non Literate: North: Pará: Belem: 5 Years to 9 Years: 6 Years data is updated yearly, averaging 16,807.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 20,386.000 Person in 1991 and a record low of 8,841.000 Person in 2010. Brazil Population: Non Literate: North: Pará: Belem: 5 Years to 9 Years: 6 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD066: Population: Non Literate: by Municipality: North: Pará: Belém.

Population: Non Literate: Southeast: São Paulo: 5 Years to 9 Years: 6 Years data was reported at 206,373.000 Person in 2010. This records a decrease from the previous number of 472,097.000 Person for 2000. Population: Non Literate: Southeast: São Paulo: 5 Years to 9 Years: 6 Years data is updated yearly, averaging 472,097.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 527,339.000 Person in 1991 and a record low of 206,373.000 Person in 2010. Population: Non Literate: Southeast: São Paulo: 5 Years to 9 Years: 6 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD097: Population: Non Literate: by Municipality: Southeast: São Paulo.

Smoking Rate as a Percentage of Population PFI No 6 by Year, Age Group and Statistic

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Brazil Population: Literate: Southeast: Minas Gerais: Belo Horizonte: 5 Years to 9 Years: 6 Years data was reported at 20,319.000 Person in 2010. This records an increase from the previous number of 15,385.000 Person for 2000. Brazil Population: Literate: Southeast: Minas Gerais: Belo Horizonte: 5 Years to 9 Years: 6 Years data is updated yearly, averaging 15,385.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 20,319.000 Person in 2010 and a record low of 9,664.000 Person in 1991. Brazil Population: Literate: Southeast: Minas Gerais: Belo Horizonte: 5 Years to 9 Years: 6 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD038: Population: Literate: by Municipality: Southeast: Minas Gerais: Belo Horizonte.

Population Aged One Year and Over Usually Resident and Present in the State Who Lived Outside the State for One Year or More (Number) by Birthplace, Country of Previous Residence, CensusYear and Year

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