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TwitterThis intermediate level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).
The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in Exploratory Data Analysis, Data Wrangling, Data Visualization and Classification Models.
Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following description gives more details on this data set:
age: the age of an individual.workclass: The type of work or employment of an individual. It can have the following categories:
Final Weight: The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex.
We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.
People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.
education: The highest level of education completed. education-num: The number of years of education completed. marital-status: The marital status. occupation: Type of work performed by an individual.relationship: The relationship status.race: The race of an individual. sex: The gender of an individual.capital-gain: The amount of capital gain (financial profit).capital-loss: The amount of capital loss an individual has incurred.hours-per-week: The number of hours works per week.native-country: The country of origin or the native country.income: The income level of an individual and serves as the target variable. It indicates whether the income is greater than $50,000 or less than or equal to $50,000, denoted as (>50K, <=50K).
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TwitterLowering average household heating energy consumption plays a pivotal role in addressing climate change and has been central to policy initiatives. Strategies proposed so far have included commitments, incentives/ disincentives, feedback, and social norms. Yet, findings so far have been mixed and fail to explain the mechanism that drives energy conservation behavior. Using a sample of 2,128 participants across the United States, we collected survey data matched with archival temperature data to investigate the influence of past experiences on current energy conservation behaviors. Our findings indicate that childhood home temperatures significantly predict current home temperature settings. Importantly, community fit moderated this relationship. Individuals with high community fit were more likely to align their home temperature settings to those of their community. These insights not only shed light on the underlying mechanisms driving energy consumption behavior but also suggest that fostering a sense of community fit might be a more effective strategy for promoting sustainable energy practices.
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This is a sample dataset derived from 2010 U.S. Government Census data. It is intended to be used in combination with example analyses on the public dataset "Iowa Liquor Sales", available as a Google Public Dataset, on Kaggle, and at https://data.iowa.gov/Sales-Distribution/Iowa-Liquor-Sales/m3tr-qhgy.
This dataset is intended for use as an example. Columns have purposely not been filtered by string manipulation in order to explore joining data between two pandas DataFrames and to do further processing.
Because this data is at the Zip Code Tabulation Area (ZCTA) level, additional processing is required to join it with general-purpose datasets, which may be specified at the zip code, county name, county FIPS code, or coordinate level. This is intentional.
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The dataset tabulates the population of Gratis by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Gratis. The dataset can be utilized to understand the population distribution of Gratis by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Gratis. 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 Gratis.
Key observations
Largest age group (population): Male # 0-4 years (74) | Female # 25-29 years (74). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Gratis Population by Gender. You can refer the same here
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The dataset tabulates the population of San Luis by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for San Luis. The dataset can be utilized to understand the population distribution of San Luis by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in San Luis. 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 San Luis.
Key observations
Largest age group (population): Male # 10-14 years (47) | Female # 35-39 years (53). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for San Luis Population by Gender. You can refer the same here
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TwitterThe Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.
All manuscripts (and other items you'd like to publish) must be submitted to
phsdatacore@stanford.edu for approval prior to journal submission.
We will check your cell sizes and citations.
For more information about how to cite PHS and PHS datasets, please visit:
https:/phsdocs.developerhub.io/need-help/citing-phs-data-core
Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.
In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.
The historic US 1920 census data was collected in January 1920. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.
Notes
We provide household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.
Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.
Coded variables derived from string variables are still in progress. These variables include: occupation and industry.
Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, MORTGAGE, FARM, CLASSWKR, OCC1950, IND1950, MARST, RACE, SEX, RELATE, MTONGUE. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.
Most inconsistent information was not edited for this release, thus there are observations outside of the universe for some variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next release.
%3C!-- --%3E
This dataset was created on 2020-01-10 18:46:34.647 by merging multiple datasets together. The source datasets for this version were:
IPUMS 1920 households: This dataset includes all households from the 1920 US census.
IPUMS 1920 persons: This dataset includes all individuals from the 1920 US census.
IPUMS 1920 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.
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Two sample datasets created for training purposes from The Squirrel Census https://www.thesquirrelcensus.com/ hosted by NYC OpenData 2018 Central Park Squirrel Census - Squirrel Data | NYC Open Data, no reuse license specified.
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Context
The dataset tabulates the population of Pittsburgh by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pittsburgh. The dataset can be utilized to understand the population distribution of Pittsburgh by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pittsburgh. 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 Pittsburgh.
Key observations
Largest age group (population): Male # 25-29 years (16,615) | Female # 20-24 years (18,291). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Pittsburgh Population by Gender. You can refer the same here
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TwitterThe dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.
The full-population dataset (with about 10 million individuals) is also distributed as open data.
The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.
Household, Individual
The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.
ssd
The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.
other
The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.
The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.
This is a synthetic dataset; the "response rate" is 100%.
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Context
The dataset tabulates the population of Indiana by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Indiana. The dataset can be utilized to understand the population distribution of Indiana by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Indiana. 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 Indiana.
Key observations
Largest age group (population): Male # 15-19 years (243,948) | Female # 15-19 years (229,391). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Indiana Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Malta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Malta. The dataset can be utilized to understand the population distribution of Malta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Malta. 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 Malta.
Key observations
Largest age group (population): Male # 35-39 years (52) | Female # 25-29 years (62). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Malta Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Southern View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Southern View. The dataset can be utilized to understand the population distribution of Southern View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Southern View. 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 Southern View.
Key observations
Largest age group (population): Male # 55-59 years (97) | Female # 25-29 years (100). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Southern View Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of New Orleans by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Orleans. The dataset can be utilized to understand the population distribution of New Orleans by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Orleans. 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 New Orleans.
Key observations
Largest age group (population): Male # 35-39 years (15,110) | Female # 30-34 years (17,105). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for New Orleans Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Lake View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lake View. The dataset can be utilized to understand the population distribution of Lake View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lake View. 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 Lake View.
Key observations
Largest age group (population): Male # 50-54 years (62) | Female # 50-54 years (75). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Lake View Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Staten Island borough by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Staten Island borough. The dataset can be utilized to understand the population distribution of Staten Island borough by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Staten Island borough. 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 Staten Island borough.
Key observations
Largest age group (population): Male # 55-59 years (16,814) | Female # 60-64 years (18,886). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Staten Island borough Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Reading town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Reading town. The dataset can be utilized to understand the population distribution of Reading town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Reading town. 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 Reading town.
Key observations
Largest age group (population): Male # 60-64 years (98) | Female # 60-64 years (120). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Reading town Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of Sheridan by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sheridan. The dataset can be utilized to understand the population distribution of Sheridan by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sheridan. 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 Sheridan.
Key observations
Largest age group (population): Male # 15-19 years (184) | Female # 50-54 years (153). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Sheridan Population by Gender. You can refer the same here
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Context
The dataset tabulates the population of New York County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New York County. The dataset can be utilized to understand the population distribution of New York County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New York County. 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 New York County.
Key observations
Largest age group (population): Male # 30-34 years (83,603) | Female # 25-29 years (92,613). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for New York County Population by Gender. You can refer the same here
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of West College Corner by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for West College Corner. The dataset can be utilized to understand the population distribution of West College Corner by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in West College Corner. 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 West College Corner.
Key observations
Largest age group (population): Male # 50-54 years (36) | Female # 25-29 years (35). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for West College Corner Population by Gender. You can refer the same here
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Sidney by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sidney. The dataset can be utilized to understand the population distribution of Sidney by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sidney. 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 Sidney.
Key observations
Largest age group (population): Male # 30-34 years (9) | Female # 50-54 years (6). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
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
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.
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/.
This dataset is a part of the main dataset for Sidney Population by Gender. You can refer the same here
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TwitterThis intermediate level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).
The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in Exploratory Data Analysis, Data Wrangling, Data Visualization and Classification Models.
Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following description gives more details on this data set:
age: the age of an individual.workclass: The type of work or employment of an individual. It can have the following categories:
Final Weight: The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex.
We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.
People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.
education: The highest level of education completed. education-num: The number of years of education completed. marital-status: The marital status. occupation: Type of work performed by an individual.relationship: The relationship status.race: The race of an individual. sex: The gender of an individual.capital-gain: The amount of capital gain (financial profit).capital-loss: The amount of capital loss an individual has incurred.hours-per-week: The number of hours works per week.native-country: The country of origin or the native country.income: The income level of an individual and serves as the target variable. It indicates whether the income is greater than $50,000 or less than or equal to $50,000, denoted as (>50K, <=50K).