Data analysis
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- Census of Population (13)
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- Canadian Housing Statistics Program (1)
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- Survey on Early Learning and Child Care Arrangements (SELCCA) (1)
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All (289)
All (289) (20 to 30 of 289 results)
- 21. Analysis 101: How to read a table ArchivedStats in brief: 89-20-00062023002Description: This video is for learners beginning their own journey to increase their current level of data literacy. No prerequisite learning is required to fully understand this video. By the end of this video, you will have a better understanding of why data tables are important, how data tables are structured and how to interpret data quality indicators within a table.Release date: 2023-10-24
- Surveys and statistical programs – Documentation: 32-26-0006Description: This report provides data quality information pertaining to the Agriculture–Population Linkage, such as sources of error, matching process, response rates, imputation rates, sampling, weighting, disclosure control methods and data quality indicators.Release date: 2023-08-25
- 23. Data Quality as Fitness for Use ArchivedStats in brief: 89-20-00062023001Description: This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use. Whether you are a new employee interested in learning the basics, or an experienced subject matter expert looking to refresh your skills, this course is here to help.Release date: 2023-07-17
- Surveys and statistical programs – Documentation: 98-20-00032021011Description: This video explains the key concepts of different levels of aggregation of income data such as household and family income; income concepts derived from key income variables such as adjusted income and equivalence scale; and statistics used for income data such as median and average income, quartiles, quintiles, deciles and percentiles.Release date: 2023-03-29
- Surveys and statistical programs – Documentation: 98-20-00032021012Description: This video builds on concepts introduced in the other videos on income. It explains key low-income concepts - Market Basket Measure (MBM), Low income measure (LIM) and Low-income cut-offs (LICO) and the indicators associated with these concepts such as the low-income gap and the low-income ratio. These concepts are used in analysis of the economic well-being of the population.Release date: 2023-03-29
- Articles and reports: 82-003-X202300200003Description: Utility scores are an important tool for evaluating health-related quality of life. Utility score norms have been published for Canadian adults, but no nationally representative utility score norms are available for non-adults. Using Health Utilities Index Mark 3 (HUI3) data from two recent cycles of the Canadian Health Measures Survey (i.e., 2016-2017 and 2018-2019), this is the first study to provide utility score norms for children aged 6 to 11 years and adolescents aged 12 to 17 years.Release date: 2023-02-15
- Articles and reports: 11-637-X202200100007Description:
As the seventh goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure access to affordable, reliable, sustainable and modern energy for all by 2030. This 2022 infographic provides an overview of indicators underlying the seventh Sustainable Development Goal in support of affordable and clean energy, and the statistics and data sources used to monitor and report on this goal in Canada.
Release date: 2022-12-13 - Articles and reports: 11-637-X202200100008Description:
As the eighth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all by 2030. This 2022 infographic provides an overview of indicators underlying the eighth Sustainable Development Goal in support of decent work and economic growth, and the statistics and data sources used to monitor and report on this goal in Canada.
Release date: 2022-12-13 - Articles and reports: 11-637-X202200100009Description:
As the ninth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation by 2030. This 2022 infographic provides an overview of indicators underlying the ninth Sustainable Development Goal in support of industry, innovation and infrastructure, and the statistics and data sources used to monitor and report on this goal in Canada.
Release date: 2022-12-13 - Articles and reports: 11-637-X202200100010Description:
As the tenth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to reduce inequalities within and among countries by 2030. This 2022 infographic provides an overview of indicators underlying the tenth Sustainable Development Goal in support of reduced inequalities, and the statistics and data sources used to monitor and report on this goal in Canada.
Release date: 2022-12-13
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Data (2)
Data (2) ((2 results))
- 1. Canadian Statistical Geospatial Explorer Hub ArchivedData Visualization: 71-607-X2020010Description: The Canadian Statistical Geospatial Explorer empowers users to discover geo enabled data holdings of Statistics Canada at various levels of geography including at the neighbourhood level. Users are able to visualize, thematically map, spatially explore and analyze, export and consume data in various formats. Users can also view the data superimposed on satellite imagery, topographic and street layers.Release date: 2024-08-21
- 2. Housing Data Viewer ArchivedData Visualization: 71-607-X2019010Description: The Housing Data Viewer is a visualization tool that allows users to explore Statistics Canada data on a map. Users can use the tool to navigate, compare and export data.Release date: 2019-10-30
Analysis (256)
Analysis (256) (40 to 50 of 256 results)
- Articles and reports: 11-522-X202100100018Description: Statistics Finland started publishing nowcasts of the trend indicator of output (TIO), the monthly indicator of real economic activity, to answer users´ needs during the Covid-19 pandemic. The indicator was first published in April 2020, at the very beginning of the pandemic in Finland, and had a monthly release schedule until June 2021. The TIO nowcasts are produced using open-source data on truck traffic volumes at about 100 automatic measuring points in the Helsinki/Uusimaa -region and the Economic Sentiment Indicator for Finland. Estimation is done using a machine learning approach and the methodology is based on previous work done by Statistics Finland and ETLA Economic Research.
Key Words: nowcasting; flash estimates; machine learning; experimental statistics.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100025Description:
We propose a longitudinal analysis with a point of view connected to the organizational changes that have taken place in the Italian National Institute of Statistics in recent years. In 2016 the Institute introduced a new Directorate, intending to standardize and generalize the business process of Data Collection according to the European standard of the GAMSO model. The paper discusses the pros and cons of this change from the perspective of the survey's participation. The ICT survey response rate analysis demonstrates an increase of around 20% since the beginning of the new organization: the paper tries to focus on the impact of the changes introduced with the new organization. We focused our attention on two specific subsets of respondents - the so-called "wanted" - the ones who have never answered to an ICT survey or to any other Istat survey and - the so-called “lost” - the ones included in two consecutive survey’s samples and that answered in the previous edition but not in the current one. The paper aims to illustrate how an efficient organization of data collection reflects its benefits on survey results and what kind of actions should be taken to catch the attention of the "wanted". Finally, we apply a logistic model measuring the probability that an enterprise responding in 2018 (t-1) also answered in 2019 (t). All the analysis suggests some actions that could be taken to improve respondents' participation, data quality, and respondents' perception of the official statistics.
Key Words: data collection strategy, response rate, paradata, response burden, ICT Survey.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100026Description:
The Government of Canada’s Directive on Open Government aims to ensure that Canadians have greater access to government data and information. One solution for open data is smart synthetic files, which retain as much analytical value as possible and take into account confidentiality issues that arise from collecting personal information. In recent years, Statistics Canada has acquired a recognized expertise in producing synthetic data files of high analytical value. In a current project, Statistics Canada is tackling a new challenge to synthesize a database and preserve hierarchical structures in the form of families, where records are linked and share common traits that must be maintained. These challenges are also encountered when synthesizing structured data such as business data. This paper presents the challenges and solutions for building synthetic data with such hierarchical structures. Application of this strategy will be illustrated with the development of a synthetic database that supports the development of retirement income policies. This database includes over 20 variables and 8 million records structured into approximately 4 million family units. We will present how family structures have been preserved, discuss the practical and technical challenges inherent in developing such a large and complex database, present the risk and utility of the data, and propose avenues for future research.
Keywords: synthetic data of high analytical value; family structures; modern data access solution.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100027Description:
Privacy concerns are a barrier to applying remote analytics, including machine learning, on sensitive data via the cloud. In this work, we use a leveled fully Homomorphic Encryption scheme to train an end-to-end supervised machine learning algorithm to classify texts while protecting the privacy of the input data points. We train our single-layer neural network on a large simulated dataset, providing a practical solution to a real-world multi-class text classification task. To improve both accuracy and training time, we train an ensemble of such classifiers in parallel using ciphertext packing.
Key Words: Privacy Preservation, Machine Learning, Encryption
Release date: 2021-10-29 - 45. Statistical Disclosure Control and Developments in Formal Privacy: In Memoriam to Chris Skinner ArchivedArticles and reports: 11-522-X202100100022Description:
I provide an overview of the evolution of Statistical Disclosure Control (SDC) research over the last decades and how it has evolved to handle the data revolution with more formal definitions of privacy. I emphasize the many contributions by Chris Skinner in the research areas of SDC. I will review his seminal research, starting in the 1990’s with his work on the release of UK Census sample microdata. This led to a wide-range of research on measuring the risk of re-identification in survey microdata through probabilistic models. I also focus on other aspects of Chris’ research in SDC. Chris was the recipient of the 2019 Waksberg Award and sadly never got a chance to present his Waksberg Lecture at the Statistics Canada International Methodology Symposium. This paper follows the outline that Chris had prepared in preparation for that lecture, and provided to me by his son, Tom Skinner. Keywords: Risk of Re-identification, Data Revolution, Privacy Models, Differential Privacy
Release date: 2021-10-22 - Articles and reports: 11-522-X202100100021Description: Istat has started a new project for the Short Term statistical processes, to satisfy the coming new EU Regulation to release estimates in a shorter time. The assessment and analysis of the current Short Term Survey on Turnover in Services (FAS) survey process, aims at identifying how the best features of the current methods and practices can be exploited to design a more “efficient” process. In particular, the project is expected to release methods that would allow important economies of scale, scope and knowledge to be applied in general to the STS productive context, usually working with a limited number of resources. The analysis of the AS-IS process revealed that the FAS survey incurs substantial E&I costs, especially due to intensive follow-up and interactive editing that is used for every type of detected errors. In this view, we tried to exploit the lessons learned by participating to the High-Level Group for the Modernisation of Official Statistics (HLG-MOS, UNECE) about the Use of Machine Learning in Official Statistics. In this work, we present a first experiment using Random Forest models to: (i) predict which units represent “suspicious” data, (ii) to assess the prediction potential use over new data and (iii) to explore data to identify hidden rules and patterns. In particular, we focus on the use of Random Forest modelling to compare some alternative methods in terms of error prediction efficiency and to address the major aspects for the new design of the E&I scheme.Release date: 2021-10-15
- Articles and reports: 12-001-X202100100003Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24 - 48. Statistics 101: Correlation and Causality ArchivedStats in brief: 89-20-00062021002Description:
This video is intended for viewers who wish to gain a basic understanding of correlation and causality. As a prerequisite, before beginning this video, we highly recommend having already completed our videos titled “What is Data? An Introduction to Data Terminology and Concepts” and “Types of Data: Understanding and Exploring Data”.
Release date: 2021-05-03 - Articles and reports: 11-633-X2021003Description: Canada continues to experience an opioid crisis. While there is solid information on the demographic and geographic characteristics of people experiencing fatal and non-fatal opioid overdoses in Canada, there is limited information on the social and economic conditions of those who experience these events. To fill this information gap, Statistics Canada collaborated with existing partnerships in British Columbia, including the BC Coroners Service, BC Stats, the BC Centre for Disease Control and the British Columbia Ministry of Health, to create the Statistics Canada British Columbia Opioid Overdose Analytical File (BC-OOAF).Release date: 2021-02-17
- Articles and reports: 11-633-X2021001Description:
Using data from the Canadian Housing Survey, this project aimed to construct a measure of social inclusion, using indicators identified by the Canada Mortgage and Housing Corporation (CMHC), to report a social inclusion score for each geographic stratum separately for dwellings that are and are not in social and affordable housing. This project also sought to examine associations between social inclusion and a set of economic, social and health variables.
Release date: 2021-01-05
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Reference (26)
Reference (26) (0 to 10 of 26 results)
- Surveys and statistical programs – Documentation: 11-633-X2026001Description: This report defines key concepts related to area-level analysis and introduces area-level measures developed and utilized at Statistics Canada for health analysis. It also provides a decision-making framework and practical recommendations to help researchers select appropriate methods. The goal is to guide readers on when area-level analysis is appropriate and what type of area-level measure is suitable to achieve research objectives.Release date: 2026-03-05
- Surveys and statistical programs – Documentation: 11-633-X2024004Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 40 years.Release date: 2024-12-09
- Surveys and statistical programs – Documentation: 11-633-X2024001Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.Release date: 2024-01-22
- Surveys and statistical programs – Documentation: 32-26-0006Description: This report provides data quality information pertaining to the Agriculture–Population Linkage, such as sources of error, matching process, response rates, imputation rates, sampling, weighting, disclosure control methods and data quality indicators.Release date: 2023-08-25
- Surveys and statistical programs – Documentation: 98-20-00032021011Description: This video explains the key concepts of different levels of aggregation of income data such as household and family income; income concepts derived from key income variables such as adjusted income and equivalence scale; and statistics used for income data such as median and average income, quartiles, quintiles, deciles and percentiles.Release date: 2023-03-29
- Surveys and statistical programs – Documentation: 98-20-00032021012Description: This video builds on concepts introduced in the other videos on income. It explains key low-income concepts - Market Basket Measure (MBM), Low income measure (LIM) and Low-income cut-offs (LICO) and the indicators associated with these concepts such as the low-income gap and the low-income ratio. These concepts are used in analysis of the economic well-being of the population.Release date: 2023-03-29
- Surveys and statistical programs – Documentation: 11-633-X2022009Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.
This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.
Release date: 2022-12-05 - Notices and consultations: 98-26-0001Description:
This white paper presents Statistics Canada’s planned approach to the 2021 Census of Population and provides a clear explanation of the processes behind the census program, touching on historical, legal, operational and content aspects. Statistics Canada recognizes that it is important to not only successfully conduct the census, but also to be transparent and informative about the way in which those efforts are accomplished. Painting a Portrait of Canada: The 2021 Census of Population gives readers an exclusive, detailed look at how census data is collected, analyzed and given back to Canadians, in the form of high-quality statistical information, used to make evidence-based decisions in Canadian society.
Release date: 2020-07-20 - Surveys and statistical programs – Documentation: 91F0015M2016012Description:
This article provides information on using family-related variables from the microdata files of Canada’s Census of Population. These files exist internally at Statistics Canada, in the Research Data Centres (RDCs), and as public-use microdata files (PUMFs). This article explains certain technical aspects of all three versions, including the creation of multi-level variables for analytical purposes.
Release date: 2016-12-22 - 10. The Data Warehouse and analytical tools to facilitate the integration of the Canadian Macroeconomic Accounts ArchivedSurveys and statistical programs – Documentation: 11-522-X201700014710Description:
The Data Warehouse has modernized the way the Canadian System of Macroeconomic Accounts (MEA) are produced and analyzed today. Its continuing evolution facilitates the amounts and types of analytical work that is done within the MEA. It brings in the needed element of harmonization and confrontation as the macroeconomic accounts move toward full integration. The improvements in quality, transparency, and timeliness have strengthened the statistics that are being disseminated.
Release date: 2016-03-24