This is a listing of raw and accumulated data, by country, and updated on a four times daily basis. Links to raw data repositories for your own research purposes: Johns Hopkins Master Data Repository (github) Cases by Country (csv) Cases by State (csv) (this table used to be displayed below, but you can view the data in summary below) Often data can be downloaded. This raw data was used to create a graph showing average daily boardings by month on the Green Line train in a news story by the Star Tribune published online on July 27, 2015. Cleaning up and making sense of data. A statistic repeats a pre-defined observation about reality. If the information collected has only numerical values, the raw data are called quantitative raw data. Raw data, also known as primary data, is data (e.g., numbers, instrument readings, figures, etc.) a web browser interface where you can view, query and download data; a web services interface described in a machine-processable format using the Statistical Data and Metadata Standard (SDMX) allowing machine-to-machine mechanisms for accessing and sharing ABS data, query, view and download data - choose pre-packaged data or customise data to your own requirements, view valuable metadata alongside the data. Frequency. Raw data is data that has not been processed for use. About. Statistics are the results of data analysis - its interpretation and presentation. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. What does raw data mean? Maths Guide now available on Google Play. It usually comes in the form of a digital data set that can be analyzed using software such as Excel, SPSS, SAS, and so on. For example, if you ask five of your friends how many pets they own, they might give you the following data: 0, 2, 1, 4, 18. They usually come in the form of a table or chart. Optionally, they may be included as comments in the header of a raw data file, … In this video we shall talk about the various data types. They might answer the questions "how much" or "how many." In the case of research data a metadata file may contain information about who, when, and how data were gathered, which machines and software were used, what the meaning and units of the variables are etc. If further issues are discovered please let us know using the "Contact us" link at the top of the page. This is what a data set looks like: It looks like you're using Internet Explorer 11 or older. One of the first things that we will want to do with a raw data set is to clean up the data to make it easier to understand. The ABS.Stat web services allow the exchange of data between computer systems, or machine-to-machine services. Raw data is the direct result of research that was conducted as part of a study or survey. Welcome to the NeoLoad 5.4 documentation. If I am interpreting your question correctly, raw data is messy, unprepared data. 2013 where to access to provide a systematic order to support exercises relating to estimate they become. Please try again or select another dataset. For example, say you needed the average MPH speed of sedans on a highway using data from a device that recorded tires passing over a reader. Raw data can be input to a computer program or used in manual procedures such as analyzing statistics from a survey. Statistical Abstract of the United States, Reported numbers and percentages in an article, Machine-readable data files, data files for statistical software programs. Raw data can be defined as set of values for various variables simply collected and stored at one point. close We will continue to load new datasets and update existing datasets in ABS.Stat as soon as possible after embargo on the data is lifted. Prices on 04/04/2019: The organization of raw data in table form with classes and frequencies. Raw data or primary data are collected directly related to their object of study (statistical units). However, data in this ABS.Stat beta release may not necessarily be the most up to date. Information and translations of raw data in the most comprehensive dictionary definitions resource on the web. This is also called as source data or atom data as it is the very original form of the data collected from the subjects or various units. “Raw data” is one of those terms that everyone in statistics and data science uses but no one defines. F = 1, FREQ = 17957; M = 2, FREQ = 11747; NR = 3, FREQ = 198. Raw data needs to undergo processing such as selective extraction, organization, and sometimes analysis and formatting to make it presentable. Raw data from online personality tests For general public edification the data collected through the personality tests on this website is dumped here. The Crime statistics datasets contain all offences against the person and property that were reported to police in that respective financial year. Criminal Justice Data Bureau of Justice Statistics. I like to take the data and apply formatting to the table to make it easier to read in Excel. Free sources include data from the Demographic Yearbook System, Joint Oil Data Inititiative, Millennium Indicators Database, National Accounts Main Aggregates Database (time series 1970- ), Social Indicators, population databases, and more. Popular statistical tables, country (area) and regional profiles . So the question is, "What are you supposed to do with this data?" Raw data is unprocessed computer data. In the context of examinations, the raw data might be described as a raw score. The socioeconomic data, as they appear in the raw statistics, show a discrepancy between a young population's legitimate aspirations for better human development and the current inadequate performance of the economies of the southern and eastern Mediterranean. collected from a source. On projects, lab exercises, and group problem-solving and discussion activities, all of which. Raw data or primary data are collected directly related to their object of study (statistical units). Sensor Raw data captured by a sensor such as the image sensor in a digital camera. Information and translations of raw data in the most comprehensive dictionary definitions resource on the web. Based on OECD data warehouse technology. An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations If you want to dig into a phenomenon, you want data. Raw Moments – The moment around origin A = 0 known as raw moment and is defined as: For ungrouped data, For grouped data, where, Notes: -> We can find first raw moment just by replacing r with 1 and second raw moment just by replacing r with 2 and so on.-> When r = 0 the moment for both grouped and ungrouped data. The data can either be entered by a user or generated by the computer itself. A number of U.N. statistical databases can be accessed for free on this site. Definition of raw data in the Definitions.net dictionary. It is also called primary or source data. Raw data that has undergone processing is sometimes referred to as cooked data. OECD Statistics Definition: Data analysis is the process of transforming raw data into usable information, often presented in the form of a published analytical … In regular conversation, both words are often used interchangeably. World Bank Data. Put in the reverse, statistics provide an interpretation and summary of data. It is a primary source. Includes macro data, industry data, international trade data, individual data, demographic and vital statistics, patent data, and more. . If you continue with this browser, you may see unexpected results. One of the ways we can increase transparency in science, in addition to posting our data, materials, and pre-registering our methods, is to start including more information about our raw data in our write-ups and reports. All rights reserved. Different kinds of raw data from Twitter, are retrieved by the Input. In cooperation with data.gov and the President's Open Governement Directive, BJS will post raw data files to this page as they become available. Obtain data and statistics in any form from raw data to publication specific to. An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations. Would you perform statistics on raw data, including duplicates, or take an average of every data point and then do statistics? 2014.. 2016.. 2017.. Part of the Open Data Census project run by Open Knowledge. Of turning raw data enthusiasts, reducing them to. Active 1 year, 3 months ago. Statistics are the results of data analysis. Viewed 451 times 0 $\begingroup$ Consider the following definitions . Data are the actual pieces of information that you collect through your study. Remember, statistics are created once data are analyzed and computations are done. A statistic will answer “how much” or “how many”. Frequency Distribution. The RDC Program posts a list of all new projects which have become active in the RDCs within the last 12 months. Population, surface area and density; PDF | CSV Updated: 5-Nov-2020; International migrants and refugees Meaning of raw data. export data in a range of formats such as Excel and CSV as well as SDMX. All data is anonymous. Raw data is extracted, analyzed, processed and used by humans or purpose-built software applications to draw conclusions, make projections or extract meaningful information. data are individual pieces of factual information recorded and used for the purpose of analysis. Such data often is important or essential for the analysis of the raw data. Leading organizations today are trying to weave raw data into business gold by understanding and exploiting the economic value of data. Raw Data And Boundaries – Applied Statistics. It is the raw information from which statistics are created. Raw data vs Primary data in statistics. Definition 1. when the information was collected by the investigator herself or himself with a definite objective in her or his mind, the data obtained is called primary data. Raw data is extracted, analyzed, processed and used by humans or purpose-built software applications to draw conclusions, make projections or extract meaningful information. U.S. Aerospace Industry Statistics. This set raw of data contains information from Bloomington Police Department regarding guns reported stolen. Search strategies and key resources to help you find data and statistical information. Raw Data and Summary Statistics Raw data for the manuscripts published by NGI investigators can be found in NIAGADS , and Synapse . This information may be stored in a file, or may just be a collection of numbers and characters stored on somewhere in the computer's hard disk. [1] My math. The proper understanding and use of statistical tools are essential to the scientific. In the world of libraries, academia, and research there is an important distinction between data and statistics. This list is updated quarterly. The Canadian Research Data Centre Network (CRDCN) hosts a bibliography of research papers published by researchers involved in the program.. Such data are called raw data. It is also called primary or source data. The web services allow the ABS to share data through a programmatic interface across the internet. Raw data, also known as source data or atomic data, is information that has not been processed in order to be displayed in any sort of presentable form. The data that you have collected for the purpose of your research is raw data. Would you perform statistics on raw data, including duplicates, or take an average of every data point and then do statistics? Such data often is important or essential for the analysis of the raw data. The number of times a certain value or class of values occurs. When working with statistics, it’s important to recognize the different types of data: numerical (discrete and continuous), categorical, and ordinal. For example, we all agree that we should be able to recreate results in scientific papers from the raw data and the code for that paper. It can be in the form of files, visual images, database records or any other digital data. We advise using Internet Explorer to view this site, there are identified issues when using other browsers. Raw Data; Best Bets for Data Data.gov. Put in the reverse, statistics provide an interpretation and summary of data. Got a question on this topic? Raw data (sometimes called source data or atomic data) is data that has not been processed for use. Statistics are generated from data by processing, organizing, analyzing, interpreting, and representing the data in a meaningful context. From the first two definitions it is obvious to observe that a raw data can be primary or secondary. © Michigan State University Board of Trustees. Population. Put in the reverse, statistics provide an interpretation and summary of data. An upgrade to the latest version of ABS.Stat is in development and will be available soon. In the table below, each row (observation) represents a business customer of a telecommunications company, and the columns (variables) represent each company’s: industry, the value that the company represents to the owner of the data, and number of employees. Raw data is primarily unstructured or unformatted repository data. Overview of Raw Data In various systems like punching machines, sales points, cash counters, trading points, hospital systems, hotel reception and admission counters, and billing systems, a lot of data is collected and stored. Environment from The World Bank: Data. The ABS privacy policy outlines how the ABS handles any personal information that you provide to us. Data Tables While many data tables are now available in data.census.gov , you can browse and download additional data tables by topic and year. Raw data, also known as primary data, is data (e.g., numbers, instrument readings, figures, etc.) Berkeley Mortality Database "This database contains life tables for national populations and, whenever available, the raw data used in constructing these tables." Statistics on crimes and criminals, prisons and jails, victims, law enforcement, … How do we determine the number of Classes? Understand raw data in the early stage of statistical analysis, as well. Typically, raw data tables are much larger than this, with more observations and more variables. The FBI crime data is fascinating and one of the most interesting data sets on this … STATISTICS. But what do we mean when we say raw data? Statistics. If you’re looking for a quick number, you want a statistic. Columns that holds specific information for each class. Raw data for statistics project. This data can be processed manually or by a machine. I am other media harvesting internet project ap statistics theresa a project, data. Applied Statistics: Freque. export data in a range of formats such as Excel and CSV as well as SDMX. Data in ABS.Stat beta will be updated as soon as possible following its 11:30 release on the ABS website. Data about development and socio-economic conditions in countries around the globe. Raw data for the manuscripts published by NGI investigators can be found in NIAGADS, and Synapse. Statistics are your place for quick numbers. There are gatherings to continuing to support resources;. Note: The BMD has been replaced by the Human Mortality Database (see below for link). Here is a method to find out the size of the raw data written to tape, the result gives a more exact view to the physical tape capacity. Raw data would be the basic numbers and details collected from research without any manipulations. The datasets listed on this page are: "raw" or unaggregated data; intended for analytic use; include crime, justice and sociodemographic variables Innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products. You find the complete Our World in Data COVID-19 dataset—together with a complete overview of our sources and more—at our GitHub repository here. Data is the raw information from which statistics are created. We have created an ABS.Stat release calendar which will give you an indication of what is available in ABS.Stat and when datasets are expected to be updated. Download the raw data that we use for our NFL tools to incorporate into your own fantasy football strategy. Definition 3. While measuring the current flow through a resistor with the help of an ammeter, there may be one... Data Manipulation. UCLA Statistics Data Sets – Some of the data that UCLA stat uses in. We have compiled a list of issues identified so far and are currently working to rectify these. Data is the raw numbers/materials collected that represent a measurement or variable; it is unorganized and unprocessed. I.E. You are advised to check the ABS website (abs.gov.au) for the latest data. Statistics result from data that have been interpreted. Elementary Statistics Making Frequency Table What is in a Frequency Distribution Table? It is represented exactly as it was captured at its source without transformation, aggregation or calculation. Statistics can be in the form of numbers or percentages and they are frequently presented in a table or graph. The following are illustrative examples. This is one of the troubles of working with raw data – it is so raw that you’re not sure what to do with it at first! This data is unprocessed or unorganized. It will also provide you with information on new datasets we plan to include in the future. After data have been collected from members of a sample or population, the information is recorded in the sequence in which it is given. Raw data for statistics project Carissa May 30, 2016. |, Search is too long (150 characters maximum), Private New Capital Expenditure and Expected Expenditure, Balance of Payments and International Investment Position, Apparent Consumption of Alcohol, Australia, Building Approvals by Statistical Area (SA2 and above), Building Approvals by Greater Capital Cities Statistical Area (GCCSA) and above, Barriers to general business activities or performance, Visitor arrivals and resident returns, reason for journey and duration of stay, Aboriginal and Torres Strait Islander Peoples, Deaths, Year of registration, Age at death, Age-specific death rates, Sex, States, Territories and Australia, Quarterly Population Estimates (ERP), by State/Territory, Sex and Age, Population Projections by Region, 2017-2066, Population Projections, Australia, 2017-2066, Regional Internal Migration Estimates by Region (SA2 and above), 2006-07 to 2015-16, RIME by region of arrival by departure, SA4 and above (ASGS 2016), 2016-17 onwards, Regional Internal Migration Estimates by LGA2011, 2006-07 to 2015-16, Regional Internal Migration Estimates by Region (SA3 and above), Age and Sex, 2006-07 to 2015-16, Regional Internal Migration Estimates by Region of Arrival and Departure (SA4 and above), 2006-07 to 2015-16, Census 2016 by Local Government Area (LGA 2016), Census 2016 by Statistical Area SA2+ (ASGS 2016), Census Time Series 2016, 2011, 2006 by Local Government Area (LGA 2016), Census Time Series 2016, 2011, 2006 by Statistical Area SA2+ (ASGS 2016), Basic Community Profiles (Local Government Areas), Basic Community Profiles (South Australia SA1), Time Series Profiles (Local Government Areas), Regional Statistics by LGA 2018, 2011-2018, Regional Statistics by LGA 2019, 2011-2019, Regional Statistics, LGA 2017, 2011-2017, Regional Statistics, ASGS 2016, 2011-2019, Regional Statistics, ASGS 2011, 2011-2016, Australian Marriage Law Postal Survey 2017 - Participation, Australian Marriage Law Postal Survey 2017 - Response, Business Indicators, Current Prices and Chain Volume Measures, Business Indicators, Current Prices and Chain Volume Measures, Percentage Changes, Inventories to Sales Ratio, Sales to Wages and Salaries Ratios and Gross Operating Profits to Sales Ratio, Actual and Expected Expenditure by Type of Asset and Industry, Current Prices, Actual and Expected Expenditure by Detailed Industries, Current Prices, Actual Expenditure by State and by Type of Asset and Industry, Chain Volume Measure, Actual Expenditure by State and by Type of Asset and Industry, Current Prices, B26 Total Family Income (weekly) by Family Composition (LGA), B26 Total Family Income (weekly) by Family Composition(SA1 SA), Census 2016, T19 Total Family Income (weekly) by Family Composition (LGA), Census 2016, T19 Total Family Income (weekly) by Family Composition (SA2+), T22 Total Family Income (weekly) by Number of Children for Couple Families (SA2+), T22 Total Family Income (weekly) by Number of Children for Couple Families (LGA), T23 Total Family Income (weekly) by Number of Children for One Parent Families (SA2+), T23 Total Family Income (weekly) by Number of Children for One Parent Families (LGA), T26 Family Composition by Income Comparison for Parents/Partners (SA2+), T26 Family Composition by Income Comparison for Parents/Partners (LGA), B26 Total Family Income (weekly) by Family Composition (SA2+), B28 Total Household Income (weekly) by Household Composition (LGA), B28 Total Household Income (weekly) by Household Composition(SA1 SA), Census 2016, T21 Total Household Income (Weekly) by Household Composition (LGA), Census 2016, T21 Total Household Income (Weekly) by Household Composition (SA2+), T24 Total Household Income (weekly) by Rent (weekly) (SA2+), T24 Total Household Income (weekly) by Rent (weekly) (LGA), B28 Total Household Income (weekly) by Household Composition (SA2+), All Finance Types by All Industries (current month), All Finance Types by All Industries (last 6 months), Finance Commitments by Purpose, All Lenders, Australia, Finance Commitments by States & Territories, B17 Total Personal Income (weekly) by Age by Sex (LGA, B17 Total Personal Income (weekly) by Age by Sex(SA1 SA), Census 2016, T13 Total Personal Income (weekly) by Age by Sex (LGA), Census 2016, T13 Total Personal Income (weekly) by Age by Sex (SA2+), B17 Total Personal Income (weekly) by Age by Sex (SA2+), Goods and Services, Chain Volume Measures, Foreign Assets and Liabilities, by Industry, Levels of Foreign Assets & Liabilities, end of period, Currency and Residual Maturity of Foreign Debt, Latest Quarter, CPI Expenditure Classes, Seasonally Adjusted, CPI Group, Sub-Group and Expenditure Class, Output of the Manufacturing industries, index numbers and percentage changes, Output of the Construction industries, subdivision and class index numbers, Selected output of the service industries, Contribution to Input of the Manufacturing Industries Index, subdivision and index points, Contribution to Input to the House construction industry index, weighted average of six state capital cities, index points, Contribution to Output to the Manufacturing Industries Index, group index points, Input to the Manufacturing industries, index numbers and percentage changes, Export Price Index by SITC, index numbers and percentage changes, Export Price Index, by Selected AHECC, BOPCE and ANZSIC, Export Price Index by Selected AHECC, SITC and ANZSIC, points contribution, Import Price Index by SITC, index numbers and percentage changes, Import Price Index, by BEC, BOPBEC, Selected HTISC, and ANZSIC, Import Price Index, by SITC, points contribution, Wage Price Index, by States and Territories, Wine available for consumption and per capita consumption, Beer available for consumption and per capita consumption, Spirits, Ready-to-Drink Beverages and Cider available for consumption and per capita consumption, Land cover net change matrix 2005-06 to 2010-11 (Hectares), Physical account for land cover 2006 to 2011 (Hectares), Land use net change matrix 2006 to 2011 Area (Hectares), Land use net change matrix 2006 to 2011 Rateable Value ($'000), Monetary account for land use 2006 to 2011, Physical account for land use 2006 to 2011 (Hectares), Land Management Practices Survey, 2011-12, Land Management Practices Survey, 2013-14, Building Approvals by Local Government Area (LGA 2017), Building Approvals by Local Government Area (LGA 2018), Building Approvals by Local Government Area (LGA 2019), Building Approvals by Local Government Area (LGA 2020), Building Approvals by Local Government Area (LGA 2016), Building Approvals, Residential by Type of Work, Number and Value of Dwelling Units Approved in New Residential Buildings, Number and Value of Non Residential Building Jobs Approved, Residential Dwellings: Unstratified Medians and Counts by Dwelling Type by GCCSA, Residential Dwellings: Values, Mean Price and Number by State and Territories, Expenditure and metres drilled by type of deposit, Monthly Retail Turnover, by Industry Group, Quarterly Retail Turnover, by Industry Group, Internet security incidents or breaches and the impact, Use of paid cloud computing and cloud services used, Factors that limited or prevented the use of paid cloud computing, Extent of IT use in business processes, by business process, by extent of use, Extent of digital technology importance, by digital technology, by extent of importance, Factors that changed business use of ICT and/or the internet, Implemented management practises for the use of ICT and/or the internet, Internet income as a percentage of total goods and services income, Geographic markets in which businesses sold goods or services, Amount of competition experienced by businesses, Businesses that sought debt or equity finance, Reasons for seeking debt or equity finance, Organisational/managerial processes innovation, Changes in business performance and activities compared to previous year, Skills used in undertaking core business activities, Skills shortage or deficiency in undertaking core business activities, B35 Type of Internet Connection by Dwelling Structure (LGA), B35 Type of Internet Connection by Dwelling Structure(SA1 SA), Census 2016, T27 Dwelling Internet Connection by Dwelling Structure (LGA), Census 2016, T27 Dwelling Internet Connection by Dwelling Structure (SA2+), Census Time Series 2016, 2011, 2006: T27 Dwelling Internet Connection by Dwelling Structure (LGA), Census Time Series 2016, 2011, 2006: T27 Dwelling Internet Connection by Dwelling Structure (SA2+), B35 Type of Internet Connection by Dwelling Structure (SA2+), Visitor arrivals and resident returns by reason for journey, Visitor arrivals and resident returns by duration of stay, Visitor arrivals and resident returns by country, B29 Number of Motor Vehicles by Dwellings (LGA), B29 Number of Motor Vehicles by Dwellings(SA1 SA), Census 2016, T22 Number of Motor Vehicles by Dwelling Records (LGA), Census 2016, T22 Number of Motor Vehicles by Dwelling Records (SA2+), Census Time Series 2016, 2011, 2006: T22 Number of Motor Vehicles by Dwelling records (LGA), Census Time Series 2016, 2011, 2006: T22 Number of Motor Vehicles by Dwelling records (SA2+), B29 Number of Motor Vehicles by Dwellings (SA2+), Australian Census Longitudinal Dataset (ACLD): Voluntary work for an organisation or group, 2006-2011, B19 Voluntary Work for an Organisation or Group by Age by Sex (LGA), B19 Voluntary Work for an Organisation or Group by Age by Sex(SA1 SA), B20 Unpaid Domestic Work: Number of Hour by Age by Sex (LGA), B20 Unpaid Domestic Work: Number of Hour by Age by Sex(SA1 SA), B21 Unpaid Assistance to a Person with a Disability by Age by Sex (LGA), B21 Unpaid Assistance to a Person with a Disability by Age by Sex(SA1 SA), B22 Unpaid Child Care by Age by Sex (LGA), B22 Unpaid Child Care by Age by Sex(SA1 SA), Census 2016, T17 Unpaid Assistance to a Person with a Disability by Age by Sex (LGA), Census 2016, T17 Unpaid Assistance to a Person with a Disability by Age by Sex (SA2+), Census 2016, T18 Unpaid Child Care by Age by Sex (LGA), Census 2016, T18 Unpaid Child Care by Age by Sex (SA2+), Census 2016, T16 Unpaid Domestic Work: Number of Hours by Age by Sex (LGA), Census 2016, T16 Unpaid Domestic Work: Number of Hours by Age by Sex (SA2+), Census 2016, T15 Voluntary Work for an Organisation or Group by Age by Sex (LGA), Census 2016, T15 Voluntary Work for an Organisation or Group by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T15 Voluntary Work for an Organisation or Group by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T15 Voluntary Work for an Organisation or Group by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T16 Unpaid Domestic Work: Number of Hours by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T16 Unpaid Domestic Work: Number of Hours by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T17 Unpaid Assistance to a Person with a Disability by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T17 Unpaid Assistance to a Person with a Disability by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T18 Unpaid Child Care by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T18 Unpaid Child Care by Age by Sex (SA2+), B19 Voluntary Work for an Organisation or Group by Age by Sex (SA2+), B20 Unpaid Domestic Work: Number of Hour by Age by Sex (SA2+), B21 Unpaid Assistance to a Person with a Disability by Age by Sex (SA2+), B22 Unpaid Child Care by Age by Sex (SA2+), B42 Labour Force Status by Age by Sex (LGA), B42 Labour Force Status by Age by Sex(SA1 SA), Census 2016, G43 Labour force status by age by sex (LGA), Census 2016, G43 Labour force status by age by sex (SA2+), Census 2016, G44 Labour force status by sex of parents by age of dependent children for Couple families (LGA), Census 2016, G44 Labour force status by sex of parents by age of dependent children for Couple families (SA2+), Census 2016, G45 Labour force status by sex of parent by age of dependent children for one parent families (LGA), Census 2016, G45 Labour force status by sex of parent by age of dependent children for one parent families (SA2+), Census Time Series 2016, 2011, 2006: T29 Selected Labour Force, Education and Migration Characteristics (LGA), Census Time Series 2016, 2011, 2006: T29 Selected Labour Force, Education and Migration Characteristics (SA2+), Census Time Series 2016, 2011, 2006: T33 Labour force status by age by sex (LGA), Census Time Series 2016, 2011, 2006: T33 Labour force status by age by sex (SA2+), T32 Labour Force Status by Age by Sex (LGA), Australian Census Longitudinal Dataset (ACLD): Labour force status, 2006-2011, B42 Labour Force Status by Age by Sex (SA2+), T32 Labour Force Status by Age by Sex (SA2+), Labour Force Status (15 Years and Over), by States and Territories, Labour Account Australia, Annual Balanced: Subdivision, Division and Total All Industries, Labour Account Australia, Annual Unbalanced: Total All Industries, B43 Industry of Employment by Age by Sex (LGA), B43 Industry of Employment by Age by Sex(SA1 SA), B44 Industry of Employment by Occupation (LGA), B44 Industry of Employment by Occupation(SA1 SA), Census 2016, G51 Industry of employment by age by sex (LGA), Census 2016, G51 Industry of employment by age by sex (SA2+), Census 2016, G52 Industry of employment by hours worked by sex (LGA), Census 2016, G52 Industry of employment by hours worked by sex (SA2+), Census 2016, G53 Industry of employment by occupation (LGA), Census 2016, G53 Industry of employment by occupation (SA2+), Census Time Series 2016, 2011, 2006: T34 Industry of employment by sex (LGA), Census Time Series 2016, 2011, 2006: T34 Industry of employment by sex (SA2+), B43 Industry of Employment by Age by Sex (SA2+), B44 Industry of Employment by Occupation (SA2+), Census 2016, G57 Occupation by Age by Sex (LGA), Census 2016, G57 Occupation by Age by Sex (SA2+), Census 2016, G58 Occupation by Hours worked by sex (LGA), Census 2016, G58 Occupation by Hours worked by sex (SA2+), Census Time Series 2016, 2011, 2006: T35 Occupation by sex (LGA), Census Time Series 2016, 2011, 2006: T35 Occupation by sex (SA2+), B46 Method of Travel to Work by Sex (LGA), B46 Method of Travel to Work by Sex (SA1 SA), Census 2016, G59 Method of travel to work by sex (LGA), Census 2016, G59 Method of travel to work by sex (SA2+), B46 Method of Travel to Work by Sex (SA2+), B07 Indigenous Status by Age by Sex (LGA), B07 Indigenous Status by Age by Sex(SA1 SA), Census 2016, T04 Indigenous Status by Age by Sex (LGA), Census 2016, T04 Indigenous Status by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T04 Indigenous Status by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T04 Indigenous Status by Age by Sex (SA2+), T06 Indigenous Status by Age by Sex (LGA), B07 Indigenous Status by Age by Sex (SA2+), T06 Indigenous Status by Age by Sex (SA2+), Projected population, Aboriginal and Torres Strait Islander Australians, Australia, state and territories, 2016 to 2031, Projected population, Aboriginal and Torres Strait Islander Australians, Indigenous Regions, 2016 to 2031, Projected population, Aboriginal and Torres Strait Islander Australians, Remotness Area, 2016 to 2031, B16 Highest Year of School Completed by Age by Sex (LGA, B16 Highest Year of School Completed by Age by Sex(SA1 SA), B40 Non-School Qualification: Level of Education by Age by Sex (LGA), B40 Non-School Qualification: Level of Education by Age by Sex(SA1 SA), B41 Non-School Qualification: Field of Study by Age by Sex (LGA), B41 Non-School Qualification: Field of Study by Age by Sex(SA1 SA), Census 2016, G46 Non-school qualification: Level of Education by age by sex (LGA), Census 2016, G46 Non-school qualification: Level of Education by age by sex (SA2+), Census 2016, G47 Non-school Qualification: Field of Study by age by sex (LGA), Census 2016, G47 Non-school Qualification: Field of Study by age by sex (SA2+), Census 2016, G48 Non-school Qualification: Field of study by occupation by sex (LGA), Census 2016, G48 Non-school Qualification: Field of study by occupation by sex (SA2+), Census 2016, G49 Non-school Qualification: Level of Education by occupation by sex (LGA), Census 2016, G49 Non-school Qualification: Level of Education by occupation by sex (SA2+), Census 2016, G50 Non-school Qualification: Level of Education by Industry of employment by sex (LGA), Census 2016, G50 Non-school Qualification: Level of Education by Industry of employment by sex (SA2+), Census 2016, T12 Highest Year of School Completed by Age by Sex (LGA), Census 2016, T12 Highest Year of School Completed by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T12 Highest Year of School Completed by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T12 Highest Year of School Completed by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T31 Non-school Qualification: Level of Education by age by sex (LGA), Census Time Series 2016, 2011, 2006: T31 Non-school Qualification: Level of Education by age by sex (SA2+), Census Time Series 2016, 2011, 2006: T32 Non-school Qualification: Field of Study by age by sex (LGA), Census Time Series 2016, 2011, 2006: T32 Non-school Qualification: Field of Study by age by sex (SA2+), T30 Non-School Qualification: Level of Education by Age by Sex (SA2+), T30 Non-School Qualification: Level of Education by Age by Sex (LGA), T31 Non-School Qualification: Field of Study by Age by Sex (SA2+), T31 Non-School Qualification: Field of Study by Age by Sex (LGA), B16 Highest Year of School Completed by Age by Sex (SA2+), B40 Non-School Qualification: Level of Education by Age by Sex (SA2+), B41 Non-School Qualification: Field of Study by Age by Sex (SA2+), B15 Type of Educational Institution Attending (Full/Part-Time Student Status by Age) by Sex (LGA), B15 Type of Educational Institution Attending (Full/Part-Time Student Status by Age) by Sex(SA1 SA), Census 2016, T11 Educational Institution: Attendee Status by Sex (LGA), Census 2016, T11 Educational Institution: Attendee Status by Sex (SA2+), Census Time Series 2016, 2011, 2006: T11 Educational Institution: Attendee Status by Sex (LGA), Census Time Series 2016, 2011, 2006: T11 Educational Institution: Attendee Status by Sex (SA2+), T13 Type of Educational Institution Attending by Sex (LGA), B15 Type of Educational Institution Attending (Full/Part-Time Student Status by Age) by Sex (SA2+), T13 Type of Educational Institution Attending by Sex (SA2+), B36 Dwelling Structure by Number of Bedrooms (LGA), B36 Dwelling Structure by Number of Bedrooms(SA1 SA), Census 2016, T24 Dwelling Structure by Dwelling Type (LGA), Census 2016, T24 Dwelling Structure by Dwelling Type (SA2+), Census 2016, T28 Dwelling Structure By Number of Bedrooms in Private Dwelling (LGA), Census 2016, T28 Dwelling Structure By Number of Bedrooms in Private Dwelling (SA2+), Census Time Series 2016, 2011, 2006: T24 Dwelling Structure by Dwelling Type (LGA), Census Time Series 2016, 2011, 2006: T24 Dwelling Structure by Dwelling Type (SA2+), Census Time Series 2016, 2011, 2006: T28 Dwelling Structure By Number Of Bedrooms in Private Dwelling (LGA), Census Time Series 2016, 2011, 2006: T28 Dwelling Structure By Number Of Bedrooms in Private Dwelling (SA2+), T14 Dwelling Structure by Household Composition and Family Composition (LGA), T15 Dwelling Structure by Number of Persons Usually Resident (SA2+), T15 Dwelling Structure by Number of Persons Usually Resident (LGA), T16 Dwelling Structure and Number of Bedrooms by Number of Persons Usually Resident for Family Households (SA2+), T16 Dwelling Structure and Number of Bedrooms by Number of Persons Usually Resident for Family Households (LGA), T17 Dwelling Structure and Number of Bedrooms by Number of Persons Usually Resident for Group Households (SA2+), T17 Dwelling Structure and Number of Bedrooms by Number of Persons Usually Resident for Group Households (LGA), B36 Dwelling Structure by Number of Bedrooms (SA2+), T14 Dwelling Structure by Household Composition and Family Composition (SA2+), B33 Mortgage Repayment (monthly) by Dwelling Structure (LGA), B33 Mortgage Repayment (monthly) by Dwelling Structure(SA1 SA), Census 2016, T25 Mortgage Repayments (monthly) by Dwelling Structure (LGA), Census 2016, T25 Mortgage Repayments (monthly) by Dwelling Structure (SA2+), Census Time Series 2016, 2011, 2006: T25 Mortgage Repayments (monthly) by Dwelling Structure (LGA), Census Time Series 2016, 2011, 2006: T25 Mortgage Repayments (monthly) by Dwelling Structure (SA2+), T25 Family Composition by Mortgage Repayment (SA2+), T25 Family Composition by Mortgage Repayment (LGA), B33 Mortgage Repayment (monthly) by Dwelling Structure (SA2+), B34 Rent (weekly) by Landlord Type(SA1 SA), Census 2016, T26 Rent (weekly) by Dwelling Structure (LGA), Census 2016, T26 Rent (weekly) by Dwelling Structure (SA2+), Census Time Series 2016, 2011, 2006: T26 Rent (weekly) by Dwelling Structure (LGA), Census Time Series 2016, 2011, 2006: T26 Rent (weekly) by Dwelling Structure (SA2+), T19 Rent (weekly) by Landlord Type (SA2+), T20 Rent (weekly) by Family Composition for Couple Families (SA2+), T20 Rent (weekly) by Family Composition for Couple Families (LGA), T21 Rent (weekly) by Family Composition for One Parent Families (SA2+), T21 Rent (weekly) by Family Composition for One Parent Families (LGA), B34 Rent (weekly) by Landlord Type (SA2+), Australian Census Longitudinal Dataset (ACLD): Tenure and landlord type, 2006-2011, B32 Tenure Type and Landlord Type by Dwelling Structure (LGA), B32 Tenure Type and Landlord Type by Dwelling Structure(SA1 SA), T18 Tenure Type and Landlord Type by Dwelling Structure (SA2+), T18 Tenure Type and Landlord Type by Dwelling Structure (LGA), B32 Tenure Type and Landlord Type by Dwelling Structure (SA2+), Housing Finance Commitments, by Lender, Percentage Change, Housing Finance Commitments, by Purpose and Change in Stock, Housing Finance Commitments, by Purpose, State and Territories, Housing Finance Commitments, by State and Territory, Housing Finance Commitments, by State and Territory, Percentage change, B08 Ancestry by Birthplace of Parents(LGA), B08 Ancestry by Birthplace of Parents(SA1 SA), B09 Country of Birth of Person by Sex (LGA), B09 Country of Birth of Person by Sex(SA1 SA), B10 Country of Birth of Person by Year of Arrival in Australia (LGA), B10 Country of Birth of Person by Year of Arrival in Australia(SA1 SA), Census 2016, T05 Ancestry by Birthplace of Parents (LGA), Census 2016, T05 Ancestry by Birthplace of Parents (SA2+), Census 2016, T06 Country of Birth of Person by Sex (LGA), Census 2016, T06 Country of Birth of Person by Sex (SA2+), Census 2016, T07 Country of Birth of Person by Year of Arrival in Australia (ranges) (LGA), Census 2016, T07 Country of birth of Person by Year of Arrival in Australia (ranges) (SA2+), T08 Country of Birth of Person by Sex (LGA), T09 Ancestry by Birthplace of Parents (LGA), B08 Ancestry by Birthplace of Parents (SA2+), B09 Country of Birth of Person by Sex (SA2+), B10 Country of Birth of Person by Year of Arrival in Australia (SA2+), T08 Country of Birth of Person by Sex (SA2+), T09 Ancestry by Birthplace of Parents (SA2+), Australian Census Longitudinal Dataset (ACLD): Unpaid assistance to a person with disability, 2006-2011, Census 2016, T14 Core Activity Need for Assistance by Age by Sex (LGA), Census 2016, T14 Core Activity Need for Assistance by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T14 Core Activity Need For Assistance by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T14 Core Activity Need For Assistance by Age by Sex (SA2+), B18 Core Activity Need for Assistance by Age by Sex (SA2+), B18 Core Activity Need for Assistance by Age by Sex (LGA), B18 Core Activity Need for Assistance by Age by Sex(SA1 SA), B23 Relationship in Household by Age by Sex (LGA), B23 Relationship in Household by Age by Sex(SA1 SA), Census 2016, T20 Family Blending by Family records (LGA), Census 2016, T20 Family Blending by Family records (SA2+), Census 2016, G60 Total family income (weekly) by labour force status of parents/partners in families (LGA), Census 2016, G60 Total family income (weekly) by labour force status of parents/partners in families (SA2+), Census Time Series 2016, 2011, 2006: T20 Family Blending by Family records (LGA), Census Time Series 2016, 2011, 2006: T20 Family Blending by Family records (SA2+), T27 Family Composition and Social Marital Status by Number of Dependent Children (SA2+), T27 Family Composition and Social Marital Status by Number of Dependent Children (LGA), T29 Family Composition and Labour Force Status of Parent(s)/Partners by Total Family Income (weekly) (SA2+), T29 Family Composition and Labour Force Status of Parent(s)/Partners by Total Family Income (weekly) (LGA), B23 Relationship in Household by Age by Sex (SA2+), B30 Household Composition by Number of Persons Usually Resident (LGA), B30 Household Composition by Number of Persons Usually Resident(SA1 SA), Census 2016, T23 Household Composition By Number Of Persons Usually Resident (LGA), Census 2016, T23 Household Composition By Number Of Persons Usually Resident (SA2+), Census Time Series 2016, 2011, 2006: T23 Household Composition By Number Of Persons Usually Resident (LGA), Census Time Series 2016, 2011, 2006: T23 Household Composition By Number Of Persons Usually Resident (SA2+), B30 Household Composition by Number of Persons Usually Resident (SA2+), B11 Proficiency in Spoken English/Language by Year of Arrival in Australia by Sex (LGA), B11 Proficiency in Spoken English/Language by Year of Arrival in Australia by Sex(SA1 SA), B12 Proficiency in Spoken English/Language of Parents by Age of Dependent Children (LGA), B12 Proficiency in Spoken English/Language of Parents by Age of Dependent Children(SA1 SA), B13 Language Spoken at Home by Sex(SA1 SA), Census 2016, T09 Language spoken at home by Sex (LGA), Census 2016, T09 Language spoken at home by Sex (SA2+), Census 2016, T08 Proficiency in spoken English/Language by Age by Sex (LGA), Census 2016, T08 Proficiency in Spoken English/Language by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T08 Proficiency in Spoken English/Language by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T08 Proficiency in Spoken English/Language by Age by Sex (SA2+), T11 Proficiency in Spoken English/Language by Age (LGA), B11 Proficiency in Spoken English/Language by Year of Arrival in Australia by Sex (SA2+), B12 Proficiency in Spoken English/Language of Parents by Age of Dependent Children (SA2+), B13 Language Spoken at Home by Sex (SA2+), T10 Language Spoken at Home by Sex (SA2+), T11 Proficiency in Spoken English/Language by Age (SA2+), B05 Registered Marital Status by Age by Sex (LGA), B05 Registered Marital Status by Age by Sex(SA1 SA), B06 Social Marital Status by Age by Sex (LGA), B06 Social Marital Status by Age by Sex(SA1 SA), Census 2016, T02 Registered Marital Status by Age by Sex (LGA), Census 2016, T02 Registered Marital Status by Age by Sex (SA2+), Census 2016, T03 Social Marital Status by Age by Sex (LGA), Census 2016, T03 Social Marital Status by Age by Sex (SA2+), Census Time Series 2016, 2011, 2006: T02 Registered Marital Status by Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T02 Registered Marital Status by Age by Sex(SA2+), Census Time Series 2016, 2011, 2006: T03 Social Marital Status Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T03 Social Marital Status Age by Sex (SA2+), T04 Registered Marital Status by Age by Sex (LGA), T05 Social Marital Status by Age by Sex (LGA), B05 Registered Marital Status by Age by Sex (SA2+), B06 Social Marital Status by Age by Sex (SA2+), T04 Registered Marital Status by Age by Sex (SA2+), T05 Social Marital Status by Age by Sex (SA2+), B01 Selected Person Characteristics (LGA), B02 Selected Medians and Averages(SA1 SA), B37 Selected Labour Force, Education and Migration Characteristics by Sex (LGA), B37 Selected Labour Force, Education and Migration Characteristics by Sex(SA1 SA), T28 Selected Labour Force, Education and Migration Characteristics (SA2+), T28 Selected Labour Force, Education and Migration Characteristics (LGA), B01 Selected Person Characteristics by Sex (SA2+), B01 Selected Person Characteristics (SA1 SA), T01 Selected Person Characteristics (SA2+), T01 Selected Person Characteristics (LGA), Census Time Series 2016, 2011, 2006: T01 Age by Sex (LGA), Census Time Series 2016, 2011, 2006: T01 Age by Sex (SA2+), B37 Selected Labour Force, Education and Migration Characteristics by Sex (SA2+), Census 2016, T10 Religious Affliation by Sex (LGA), Census 2016, T10 Religious Affiliation by Sex (SA2+), Births, by year and month of occurrence, by state, Aboriginal and Torres Strait Islander births and confinements, summary, by state, Aboriginal and Torres Strait Islander fertility, by age, by state, B24 Number of Children Ever Born by Age and Sex of Parent (SA2+), B24 Number of Children Ever Born by Age of Parent (LGA), B24 Number of Children Ever Born Night by Sex (SA1 SA), T07 Number of Children Ever Born by Age of Parent, T07 Number of Children Ever Born by Age of Parent (LGA), Deaths, Year of registration, Summary data, Sex, States, Territories and Australia, Deaths, Year of registration, Age at death, Sex, Australia, Deaths,Year of occurrence, Age at death, Age-specific death rates, Sex, States, Territories, and Australia, Deaths, Year of registration, Marital status, Age at death, Sex, Australia, Infant deaths and Infant mortality rates, Year of registration, Age at death, Sex, States, Territories and Australia, Deaths, Year of registration, Indigenous status, Summary data, Sex, States, Territories and Australia, Deaths and Infant deaths, Year and months of occurrence, Sex, States, Territories and Australia, Deaths, Year of registration, Indigenous status, Age at death, Sex, Five State/Territory, Infant deaths, Year of occurrence, Age at death, Sex, Australia, ERP by LGA (ASGS 2018), Age by Sex, 2001 to 2018, ERP by LGA (ASGS 2017), Age and Sex, 2001 to 2017, ERP by LGA (ASGS 2019), Age and Sex, 2001 to 2019, ERP and components by LGA (ASGS 2017), 2017, ERP by LGA (ASGS 2016), Age and Sex, 2001 to 2016, ERP and components by LGA (ASGS 2018), 2017 to 2018, ERP and components by LGA (ASGS 2019), 2017 to 2019, ERP by SA2 and above (ASGS 2016), 2001 onwards, ERP and components by SA2 and above (ASGS 2016), 2017 onwards, ERP by SA2 (ASGS 2016), Age and Sex, 2001 Onwards, ERP by SA2 and above (ASGS 2011), 1991 to 2016, ERP by SA2 (ASGS 2011), Age and Sex, 2001 to 2016, Estimated resident population, Country of birth, Age and sex - as at 30 June 1996 to 2019, Estimated resident population, Country of birth, State/territory, Age and sex - as at 30 June 1996 to 2016, Census years, Quarterly Population Estimates (ERP) by State/Territory, Quarterly Population Estimates (ERP) by Sex and Age, Interstate Migration by States and Territories of Arrival and Departure by Sex, Interstate migration: Arrivals, departures and net, State/territory, Age and sex - Calendar years, 1997 onwards, Interstate migration: Arrivals, departures and net, State/territory, Age and sex - Financial years, 1996-97 onwards, Net overseas migration, Arrivals, departures and net, State/territory, Age and sex - Calendar years, 2004 onwards, Net overseas migration: Arrivals, departures and net, State/territory, Age and sex - Financial years, 2004-05 onwards, Net overseas migration, Arrivals, departures and net, State/territory, Major groupings and visas - Calendar years, 2004 onwards, Net overseas migration, Arrivals, departures and net, State/territory, Major groupings and visas - Financial years, 2004-05 onwards, Population Projections by Region, New South Wales, Population Projections by Region, Victoria, Population Projections by Region, Queensland, Population Projections by Region, South Australia, Population Projections by Region, Western Australia, Population Projections by Region, Tasmania, Population Projections by Region, Northern Territory, Population Projections by Region, Australian Capital Territory, Population Projections by Age, Australia, 2017-2066, Projected families, Australia, 2016 to 2041, Projected households, Australia, 2016 to 2041, Projected persons, by living arrangement, Australia, 2016 to 2041, Net internal and overseas migration estimates by region (SA2 and above) and age, 2016-17 onwards, B03 Place of Usual Residence on Census Night by Sex(SA1 SA), B38 Place of Usual Residence 1 Year Ago by Sex (LGA), B38 Place of Usual Residence 1 Year Ago by Sex(SA1 SA), B39 Place of Usual Residence 5 Years Ago by Sex (LGA), B39 Place of Usual Residence 5 Years Ago by Sex(SA1 SA), B03 Place of Usual Residence on Census Night by Age (LGA), B03 Place of Usual Residence on Census Night by Age (SA2+), B38 Place of Usual Residence 1 Year Ago by Sex (SA2+), B39 Place of Usual Residence 5 Years Ago by Sex (SA2+), B24 Number of Children Ever Born by Age of Parent (SA2+), B03 Place of Usual Residence on Census Night by AgeSA1 SA), Regional Statistics by LGA-Population and People, Regional Statistics by LGA-Economy and Industry, Regional Statistics by LGA-Education and Employment, Regional Statistics by LGA-Health, Disability, Family and Community, Regional Statistics by LGA-Persons Born Overseas, Regional Statistics by LGA-Land and Environment, Regional Statistics by LGA-Income(Including Government Allowances), Regional Statistics by LGA - Population and People, Regional Statistics by LGA - Economy and Industry, Regional Statistics by LGA - Income (Including Government Allowances, Regional Statistics by LGA - Education and Employment, Regional Statistics by LGA - Health, Disability, Family and Community, Regional Statistics by LGA - Persons Born Overseas, Regional Statistics by LGA - Land and Environment, Regional Statistics by LGA - Income(Including Government Allowances), Regional Statistics by LGA - Education and Employment Allowances), Regional Statistics by ASGS - Population and People, Regional Statistics by ASGS - Aboriginal And Torres Strait Islander Peoples, Regional Statistics by ASGS - Economy and Industry, Regional Statistics by ASGS - Income (Including Government Allowances), Regional Statistics by ASGS - Education and Employment, Regional Statistics by ASGS - Health, Disability, Family and Community, Regional Statistics by ASGS - Persons Born Overseas, Regional Statistics by ASGS - Land and Environment, Regional Statistics by ASGS - Income (Inculding Government Allowances), Regional Statistics by ASGS (Hierarchical), Regional Statistics by LGA (Hierarchical), SEIFA 2016 by Local Government Area (LGA), SEIFA 2016 by State Suburb Code (SSC) - NSW, SEIFA 2016 by State Suburb Code (SSC) - VIC, SEIFA 2016 by State Suburb Code (SSC) - QLD, SEIFA 2016 by State Suburb Code (SSC) - SA, SEIFA 2016 by State Suburb Code (SSC) - WA, SEIFA 2016 by State Suburb Code (SSC) - TAS, SEIFA 2016 by State Suburb Code (SSC) - NT, ACT and Other Territories, SEIFA 2016 by NT, ACT and Other Territories SA1, SEIFA 2011 by Local Government Area (LGA), SEIFA 2011 by NT, ACT and Other Territories SA1, SEIFA 2011 by Statistical Local Area (SLA), Australian Marriage Law Postal Survey - Participation, Table 1: Participation by State and Territory, Persons and Age, Table 2: Participation by State and Territory, Males and Age, Table 3: Participation by State and Territory, Females and Age, Table 4: Participation by Federal Electoral Division, Persons and Age, Table 5: Participation by Federal Electoral Division, Males and Age, Table 6: Participation by Federal Electoral Division, Females and Age, Australian Marriage Law Postal Survey - Response, Table 2: Response by Federal Electoral Division. 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