Quarterly Financial Reports
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- Supplementary data
Statistics Canada was established to ensure that Canadians have access to a trusted source of statistics on Canada to meet their highest priority needs. Access to trusted statistical information underpins democratic societies, as it supports evidence-based decision-making in the public and private sectors, and informs debate on public policy issues.
As a department, Statistics Canada is responsible for the following:
All of the government's Acts and Regulations can be found on the Justice Laws website.
To learn about upcoming or ongoing consultations on proposed federal regulations, visit the Canada Gazette and Consulting with Canadians websites.
CCOFOG data are presented for all general government sectors: the federal general government sector, the provincial general and territorial government sector, the local general government sector, the colleges and universities sector, and the health, school board and Canada and Quebec Pension Plan sectors. Canadian Classification of the Functions of Government (CCOFOG) coding is applied at the program level for the general ledger accounts, specified purpose accounts, special funds, and income statements of specific entities, such as colleges and universities.
For the province of Nova Scotia, program expenses were estimated from the provincial general government budget documents. This is similar to the methodology used in the previous Financial Management System (FMS) framework.
The published CCOFOG data represent only expenses, with the exception of the consumption of fixed capital. They also exclude acquisitions of non-financial assets. The CCOFOG data are currently available for the period 2008 to 2012.
CCOFOG provisional data are being released for the first time in November 2014. This provisional qualifier signals to users that although the data are fit for use, they are subject to revisions. Over the next year these data will be integrated into the rest of the Canadian System of Macroeconomic Accounts (the National Accounts, Balance of Payments, International Investment Position, Input-Output Tables) resulting in revisions as data, concepts and methods are reconciled and aligned within the national accounts framework.
A CCOFOG-based analysis must be limited exclusively to a chronological analysis of a single jurisdiction in a single government sector. Transfers among jurisdictions and among government sectors are not consolidated, which means that, in practice, if the health and education sectors, for example, are added to the general provincial government sector, we are over-estimating expenses as a result of the double-counting of the value of the transfers.
Inter-provincial comparisons are also strictly impossible because the non-reconciliation of transfers means that we cannot compare a government sector that has different responsibilities in two provinces. For example, Ontario delegated a majority of its social housing responsibilities to the local government sector, but British Columbia did not. Thus, comparing CCOFOG data from division 710 – Social Protection, for the general government sector for these two provinces is statistically invalid.
The CCOFOG classification has three levels. The highest level is referred to as the division and has 10 separate categories. The second level is referred to as the group and the lowest level is referred to as the class. In this initial CCOFOG release the data are presented at the division level and exclude amortization of non-financial assets expenditures. In November 2015, the data will be presented at the group level.
The primary mandate of a government's program, together with additional information provided by the Canadian Government Finance Statistics (CGFS) coding, is used to assign the CCOFOG classification. When a program has multiple mandates requiring multiple CCOFOG codes, available financial documents are used to determine the main proportion of the observed expense. The total value of the government's program is then assigned to that CCOFOG code.
In general, special funds usually have a single function and thus a single CCOFOG code is assigned. For example, a social housing authority would have all expenses coded to 71069 – Housing.
The assignment is always at the lowest level of CCOFOG detail, which is the class level.
The 2014 Government Finance Statistics Manual, published by the International Monetary Fund, provides an overview of the COFOG assignment rules in Chapter 6 and its annex. Canada rigorously adheres to the guidelines described in the manual but has introduced certain nuances that more accurately reflect the Canadian reality. The “Detailed assignment decisions” section explains these nuances by class and/or function.
When a program significantly impacts a number of different classes in the same group, or if there wasn't enough detail, an aggregate was sometimes created. For example, aggregate 70459 – Transport n.e.c. was created to represent the sum of transport expenses that could not be specifically allocated to the Road Transport, Water Transport, Railway Transport, Air Transport and Pipelines and other transport systems classes.
Division 701- General public services
Centralized services such as Access Ontario are classified under 70133 – Other general services. Services shared by certain departments, such as information technology and human resources, are deemed to be “centralized” if they cover more than two departments.
Government research institutes are generally classified under Basic research (70149); most other research institutes are assigned to applied research or experimental development in their area of expertise (health, agriculture, etc.).
All negotiations of territorial treaties with Aboriginal bands are included in class 70169 – General public services.
All expenses identified under the CGFS classification as interest expense, are classified under 70179 – Public debt transactions.
Transfers to governments for infrastructure expenses are coded under group 7018 – Transfers of a general character between different levels of government. Code 70181 was created to identify transfers to the federal government, while code 70182 identifies transfers to provincial governments and code 70183, transfers to local governments.
Division 702- Defence
Military defence is exclusively a federal government jurisdiction – these expenses will not be found at the provincial/territorial or local level.
Division 703- Public order and safety
In Canada, probation and parole monitoring programs are the responsibility of prison administrations and not the courts as recommended by the Government Finance Statistics Manual. To preserve the comparability of international data, we have left these programs under the courts, but we have set them apart by identifying them by a specific code, 70331. This code will make it easier to transfer the program when Canada publishes its public order and safety expenses under its Justice framework.
Similarly, two key Public order and safety programs in Canada also received their own unique codes: 70332 for legal aid and 70333 for administrative tribunals.
704- Economic affairs
Expenses related to status of women boards and other gender equality initiatives are included in 70412- General labour affairs because, historically, the employment component was the initial focus of these programs.
A CCOFOG group was created to integrate the expenses of programs involving immigration and citizenship, namely, 70413 – Citizenship and immigration.
As mentioned earlier, a special aggregation was created to combine transport expenses when there is not enough detail to identify a specific class: 70459 – Transport n.e.c.
705 – Environmental protection
At the local government level, it is sometimes difficult to separate water supply (70639) and waste water management (70529) expenses; in these instances, a new CCOFOG classification was created to aggregate the two types of expenses (70631).
706 – Housing and community amenities
At the local government level, it is sometimes difficult to separate water supply (70639) and waste water management (70529) expenses; in these instances, a new CCOFOG classification was created to aggregate the two types of expenses (70631).
707 – Health
708 – Recreation, culture and religion
709 – Education
The level of available detail in our source data on education expenses does not allow us to estimate pre-elementary and elementary data or the first and second cycles at the secondary level. We have therefore grouped these classes together in a new aggregated category, 70929 – Elementary and secondary education.
We are also unable at this time to separate non-doctoral higher education (70941) from doctoral (70942); we have therefore combined these two classes into a new aggregated category University education (70949).
Furthermore, when there was not sufficient detail to distinguish college education (70939) from university education (70949), the default choice was to classify this expense under university education (70949).
Internships and apprenticeships were included in division 709 – Education only when such hours were essential to obtain credits toward the degree or diploma. Otherwise, internships and apprenticeships are included in division 704 – Economic affairs under group 70412 – General labour affairs.
710 – Social protection
To accommodate the requirements of public order and safety expenses, under group 7107 – Social exclusion n.e.c. a new class was created 71071 – Victims of crime.
The hierarchy of the public sector along with its subcomponents.
Public sector:
Footnotes
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NAPCS Canada 2012 Version 1.1 updates NAPCS Canada 2012 Version 1.0. In total, there are 271 changes between the two versions. Some categories were split, and others were merged. New categories were incorporated, and some were deleted, for a net addition of 65 product categories at different levels, providing better alignment with survey data collection and publication. In some instances, these modifications created the need to renumber codes. These changes account for about half of the update. The remaining changes relate to editing of class titles adding precision to their formulations. The detailed list of changes can be obtained from Standards Division at standards-normes@statcan.gc.ca.
The standard classification structure of NAPCS Canada 2012 comprises four levels: group, class, subclass, and detail. The table below outlines the nomenclature and provides the number of categories within each level of NAPCS Canada 2012 versions 1.1 and 1.0.
Level | Coding | Number of categories NAPCS 2012 Version 1.1 | Number of categories NAPCS 2012 Version 1.0 |
---|---|---|---|
Group | 3-digit codes | 158 | 158 |
Class | 5-digit codes | 511 | 510 |
Subclass | 6-digit codes | 1,402 | 1,398 |
Detail | 7-digit codes | 2,694 | 2,648 |
Table source: Statistics Canada, NAPCS. |
Along with NAPCS Canada 2012 Version 1.1, two new regrouping variants are made available: one for the Industrial Product Price Index (IPPI) and one for the Raw Materials Price Index (RMPI). These variants add one level (section) above the standard classification structure; this new level is defined in terms of standard groups (three-digit). The tables below illustrate the nomenclature of the IPPI and RMPI variants and provide the number of categories within each level.
Levels for IPPI variant | Coding | Number of categories |
---|---|---|
Section | 3-character alphanumeric codes | 21 |
Group | 3-digit standard codes, and 4-character alphanumeric codes | 79 |
Class | 5-digit standard codes, and 6-character alphanumeric codes | 241 |
Subclass | 6-digit standard codes | 665 |
Detail | 7-digit standard codes | 1,190 |
Table source: Statistics Canada, NAPCS. |
Levels for RMPI variant | Coding | Number of categories |
---|---|---|
Section | 3-character alphanumeric codes | 6 |
Group | 3-digit standard codes, and 4-character alphanumeric codes | 21 |
Class | 5-digit standard codes, and 6-character alphanumeric codes | 44 |
Subclass | 6-digit standard codes | 90 |
Detail | 7-digit standard codes | 197 |
Table source: Statistics Canada, NAPCS. |
At the time of publishing this note, the plan is to create two regrouping variants for capital expenditures and one extension variant for agricultural products.
This standard was approved as a departmental standard on January 16, 2007.
The Standard Geographical Classification (SGC) is Statistics Canada's official classification of geographic areas in Canada. The SGC provides unique numeric codes for three types of geographic areas: provinces and territories, census divisions (counties, regional municipalities), and census subdivisions (municipalities). The three geographic areas are hierarchically related; a seven-digit code is used to show this relationship. In addition to the SGC units, metropolitan areas with their component census subdivisions and economic regions with their component census divisions are included.
The Standard Geographical Classification (SGC) is Statistics Canada's official classification of geographic areas in Canada. The SGC provides unique numeric codes for three types of geographic areas: provinces and territories, census divisions (counties, regional municipalities), and census subdivisions (municipalities). The three geographic areas are hierarchically related; a seven-digit code is used to show this relationship. In addition to the SGC units, metropolitan areas with their component census subdivisions are included.
Survival after a diagnosis of cancer is affected by a variety of individual, tumour and healthcare system factors. Individual factors include sex, age at diagnosis, comorbidity, socioeconomic status and lifestyle; tumour-related factors include histological subtype, aggressiveness of the tumour, and spread of disease at diagnosis; and, healthcare system factors include the availability and quality of early detection, diagnostic and treatment services. Examined across cancer types and regions, survival estimates can be used to establish priority areas for improving prognosis.Note 1 Examined over time, and in conjunction with incidence and mortality trends, survival estimates can be used to monitor progress in cancer control.Note 2 Because of the importance of cancer survival, the Canadian Cancer Registry (CCR) regularly produces survival estimates using the cohort approach. Key aspects of the methodology employed are detailed below.
Survival analyses include all primary cancers, including multiple primaries for the same person. This approach is becoming standard practice.Note 3-5 However, cancers diagnosed through autopsy only or death certificate only (DCO) are excluded from survival analysis because the date of diagnosis, and thus survival time, is unknown. Since the “true” survival of cases registered as DCO is generally poorer than those registered by other means, the common approach of excluding DCOs may bias survival estimates upward, particularly in provinces/territories with proportionally more DCOs. The magnitude of such bias, however, is generally minor.Note 6
The vital status of a person with cancer is determined through linkage with the Canadian Vital Statistics Death Database and information reported by provincial/territorial cancer registries (PTCRs). Deaths reported by PTCRs but not confirmed by linkage are included in survival analyses using the date of death submitted by PTCRs. Survival time is calculated as the number of days between the date of diagnosis and date of death or date of last follow-up (whichever is earliest). For the small percentage of persons missing month and/or day of diagnosis or death, the survival time is estimatedNote 7; however, decedents with an unknown year of death are excluded from survival analyses.
Survival analyses are performed using publicly available SAS programs to which minor adaptations are made.Note 8 The standard five-year observation time for each individual is split into multiple observations, one for each interval of follow-up time. Three month intervals are used for the first year of follow-up and six month intervals for the remaining four years for a total of 12 intervals. Since the employed actuarial life table method assumes deaths are evenly distributed within an interval, more intervals are used in the first year of follow-up because mortality is often highest and most unevenly distributed during the first year after a cancer diagnosis. With the exception of cases previously excluded because they were diagnosed through autopsy only or DCO, persons with the same date of diagnosis and death are assigned one day of survival because the SAS program automatically excludes cases with zero days of survival. Survival estimates are then calculated at discrete points in follow-up by taking the product of the interval-specific (conditional) survival estimates within the follow-up period.
Expected survival proportions are derived from sex- and province/territory-specific annual life tables by applying the Ederer II approach.Note 9 Due to small populations, only abridged life tables are produced for Prince Edward Island and the three territories. Using methods suggested by Dickman et al.Note 10, abridged life tables are expanded to complete life tables using the abridged and complete life tables for Canada. Since abridged life tables only extend to age 99 years, expected survival proportions for age 100 to 109 years are drawn from complete Canadian life tables.
Five-year observed survival is the percentage of people surviving five-years after cancer diagnosis. Five-year relative survival ratios are estimated as the ratio of the observed survival of the group diagnosed with cancer to the expected survival for the corresponding general population of the same age, sex, province of residence, and time period. In theory, relative survival ratios greater than 100% indicate that the observed survival of people with cancer is better than that expected in a comparable group from the general population. In these instances, it could be that the persons diagnosed with cancer experienced lower mortality from other causes of death because of a greater than usual interaction with the healthcare system. However, estimates of relative survival greater than 100% should be interpreted with caution as several other factors may be at play including random variation in the observed number of deaths, failure to register some cancer deaths, and imprecision in the estimation of expected survival.
As an indication of the level of statistical uncertainty in survival estimates, confidence intervals formed from standard errors estimated using Greenwood's methodNote 11 are provided. To avoid implausible lower limits less than zero and/or upper limits greater than one for observed survival estimates, asymmetric confidence intervals based on the log (-log) transformation are constructed. Relative survival ratio confidence limits are then derived by dividing the observed survival limits by the corresponding expected survival proportion.
Because survival estimates vary with age and the age distribution of cancer cases can vary over time and between geographic areas, it is usually preferable to use age-standardized survival estimates to compare survival over time, across provinces, or between a province and Canada as a whole. Age-standardized survival estimates are interpretable as the survival estimate that would have occurred if the age distribution of the cancer group under study had been the same as that of the standard population. Age-standardized estimates are calculated using the direct method. Specifically, age-specific survival estimates for a given cancer are weighted to the age distribution of persons diagnosed with that cancer over a recent, relatively long period with the age categories used in the weighting being dependent on the cancer under study. Such an approach has the advantage of producing age-standardized survival estimates that are similar to non-standardized estimates.Note 12 Specifics regarding the standard population used and age categories employed are generally detailed in the various publications released by Statistics Canada. Confidence intervals for age-standardized relative survival ratios are formed by multiplying the corresponding age-standardized observed survival lower and upper limits by the ratio of the age-standardized relative survival ratio to the age-standardized observed survival.
Our virtual information sessions will take place during the winter of 2025. Details will be announced at a later date.
Recruitment - Mathematical Statisticians
The Mathematical Statisticians group is not launching a recruitment campaign at this time. Future opportunities will be shared on our website. We encourage you to visit our page for updates or sign up for email notifications through jobs.gc.ca.
Consult the links below for general information about the position and the application process.
At Statistics Canada, we produce and interpret a high volume of statistical information. We sample, collect, acquire, clean, correct, combine and analyze data to explain statistical information related to many aspects of Canada's economy and society. We produce statistics by conducting censuses every five years and by conducting numerous surveys, but also by exploiting data coming from a very broad range of data sources. We develop and use innovative solutions including machine learning and big data, state-of-the-art statistical theories and leading-edge statistical methods.
Statistics Canada is currently undertaking a significant transformation. Outcomes of this transformation will be an agency that is responsive to emerging data needs, increases the statistical literacy of Canadians and facilitates the responsible use of data for decision making. Innovative statistical methods are now more than ever essential to our success. A very knowledgeable, talented and diversified workforce is at the core of this transformation and Mathematical Statisticians play a key role.
Mathematical Statisticians apply, adapt and develop mathematical, statistical or survey methods to practical problems. They explore and adopt sophisticated methods to integrate and transform alternate data sources into statistical information. Their work is crucial to Statistics Canada. The quality of data outputs and the costs of operations are heavily dependent on the methodology used.
As a mathematical statistician, your main duties will consist of designing, implementing and evaluating statistical methods related to the production of official statistics. This could include work on surveys, work on research projects in various areas related to statistical methods, or work on new methods such as integrating data from a variety of existing sources or adopting new methods to analyze data. Mathematical Statisticians can also be involved in projects related to the combination of classic and leading edge statistical methods, including experimentation with machine learning, artificial intelligence techniques, non-probabilistic sampling, micro simulation and modeled/synthetic data. Mathematical Statisticians face a wide range of theoretical and practical statistical challenges!
Statistics Canada projects are normally developed through multidisciplinary project teams. Such teams can include experts from various area: subject-matter (e.g., economists, sociologists, geographers), survey operations, systems development and methodology (mathematical statisticians). This structure requires mathematical statisticians to acquire some knowledge of the client area in order to determine and meet its methodological needs.
Within a team, the Mathematical Statistician brings his or her expertise, experience and a critical, analytical mind to the area of statistical and survey methods. In the multidisciplinary project structure, he or she is primarily a service provider. The work of a Mathematical Statistician is highly diversified and requires creativity and adaptability. Individual Mathematical Statisticians usually work on several projects or activities at the same time. Each project is a new challenge; ready-made solutions in the literature can rarely be directly applied. To design and implement effective, scientifically sound methodology, a Mathematical Statistician must always maintain a good balance of skills in analytical and empirical research and operational work. The majority of Mathematical Statisticians provide methodological support services while some of them are also involved in research.
Like any organization striving to be the best in its field, Statistics Canada places great emphasis on research. The organization has a long and rich tradition of research in the area of statistical methods. In particular, methodological research focuses on cutting-edge ways to produce reliable statistics at a lower cost.
Statistics Canada provides the environment and support that enable Mathematical Statisticians to deal with all types of research problems, whether they are associated with a specific project, or are more general in nature.
Mathematical Statisticians regularly conduct theoretical studies and empirical simulations to support the methodological services they provide to project teams. They also carry out research on a variety of subjects related to statistical methods, such as spatial and temporal estimation, non-sampling variance, outlier detection, benchmarking, interpolation and calendarization, longitudinal data analysis, and time series. Initially, research could make up only a small part of the duties of a mathematical statistician. However, it could become a major part of his/her duties depending on his/her experience and interests.
Results of mathematical statisticians' research projects may be presented at relevant conferences and published in technical journals. One of these is Statistics Canada's own internationally renowned journal, Survey Methodology.
To foster and promote research, Statistics Canada has established an external fellowship program. In addition, Statistics Canada is actively involved in joint research projects with universities and with statistical agencies in other countries.
As a new employee, you will work closely with a more experienced Mathematical Statistician which will allow you to gain valuable experience that will enhance your professional skills.
Statistics Canada is committed to offering its employees a modern and flexible workplace. A great importance is placed on the well-being of employees. We have numerous programs and facilities designed for the benefit of employees. Health and safety, respect and fairness, flexible work arrangements, a sense of belonging and recognition and workplace wellness are at the heart of our organizational culture.
Statistics Canada gives high priority to human resource training and development. Employees are encouraged to develop their interests and are supported throughout their career. Statistics Canada offers a complete, well-organized development program in both official languages.
Our statistical training program is large, varied and includes courses or other learning activities of high quality. In the current modernization context at Statistics Canada, mathematical-statisticians must maintain and develop their capacities related to traditional survey methods, while making room for leading-edge methods, data integration and data science.
One of the goals of our training and development program is to build a culture of productive learning. The courses in the classic classroom format are only one element among many of our training tools, that also include self-directed, informal, and hands on training.
A number of courses on statistics and survey methodology are offered on a regular basis by guest instructors or experienced Statistics Canada employees. The courses cover both classical theory and the results of recent research. To provide technical and professional support, a complete range of courses is also offered on other subjects as varied as informatics, project management, employee supervision and presentation skills.
All new employees also participate in a two-week, full-time course called Survey Skills Exploration Course. Participants work in teams to design a sample survey on a predetermined socio-economic topic thereby increasing their awareness of the policies, principles, complexities, and interrelationships inherent in the design of a statistical survey. This practical training is complemented by classroom sessions that provide additional knowledge on survey methods and procedures.
Mathematical statisticians are encouraged to present the results of their work at seminars and at relevant conferences, and to submit articles to technical journals. In addition to publishing the journal Survey Methodology, Statistics Canada holds an annual symposium on a topical theme related to statistical methods.
Statistics Canada encourages employees to continue their professional development by taking academic courses relevant to their job. Three universities in the Ottawa-Gatineau area allow employees to improve their knowledge. As well as regular courses offered by local universities, there are programs customized for Statistics Canada personnel. Furthermore, on occasion, Statistics Canada grants education leave to employees. These employees have the possibility of full-time education leave to pursue an additional university degree in statistics or a related field.
Generally speaking, all candidates are hired at the MA -2 level. The starting salary of a mathematical statistician is $67,476Footnote *. After 16 to 24 months experience at Statistics Canada, the salary reaches the first step of the MA-3 level. Annual pay increments then take place within the MA-3 level until the maximum salary is reached.
Level | Position | Promotion | Pay rate |
---|---|---|---|
MA-2 | MethodologistFootnote 1 | Recruitment | $67,476 to $80,814 |
MA-3 | Methodologist | PromotedFootnote 2 from MA-2 typically after 16 to 24 months | $82,148 to $96,779 |
MA-4 | Senior Methodologist | Selection process | $98,091 to $114,403 |
MA-5 | Senior Methodologist | Selection process | $114,914 to $130,485 |
MA-6 | Section Chief | Selection process | $128,014 to $144,531 |
MA-7 | Assistant Director | Selection process | $140,171 to $156,872 |
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A registered pension plan, a dental care plan, a health care plan, a disability insurance plan, and life insurance are included in the benefits of the employees of the federal public service. As a new employee, you are entitled every year to 20 days of vacation leave, 15 days of sick leave, 5 days of leave for family-related responsibilities and 2 days of leave for a personal reason. Maternity/parental leave and up to 5 years of leave without pay for childcare and eldercare are also available.
Persons residing in Canada and Canadian citizens residing abroad. Preference will be given to veterans, Canadian citizens and permanent residents.
Step 1: Applications are online through the Public Service Commission website (Government of Canada jobs).
Step 2: Write the test.
Step 3: Successful candidates will be invited to an interview.
Upon request, you must be able to provide the following documents:
Candidates must be able to demonstrate the following:
The written test is evaluating knowledge AND ability to communicate in writing. It consists of two parts. Part A contains one question to test writing ability. Part B tests knowledge and contains multiple choice questions, fill-in-the-blank questions and an open question. There is no break period between the two parts of the test.
Examples of questions similar to those found on the test are given below.
Please note that tests from previous years are not available.
Example 1
Prepare a letter of approximately 200 to 300 words to the director of recruitment for Statistics Canada in which you explain how your training, work experience and interpersonal skills make you a strong candidate for a position as a mathematical statistician.
Example 2
In 2002 and 2007, public health officials conducted a survey on the lifestyle choices of members of your community. The following table shows an extract of the official results of that survey:
Year | Population (Number of Adults) | Estimated number of "smoking" adults | Estimated number of adults with "hypertension" | Estimated number of "smoking adults with "hypertension" |
---|---|---|---|---|
2007 | 5,000 | 300 | 350 | 250 |
2002 | 4,000 | 400 | 400 | 200 |
As a journalist involved in community affairs, you have followed this story closely from the beginning. Therefore, your editor-in-chief has asked you to write an article for your newspaper which explains the survey results to your readers.
Please write this article in 200 to 300 words.
1. In a batch of 10 items, we wish to extract a sample of 3 without replacement. How many different samples can we extract?
Answer: 10! / (7!*3!) = 10*9*8 / (3*2*1) = 120
2. The difference between the parameter we wish to estimate and the expected value of its estimator is __________.
Answer: the bias
3. Let X and Y be independent random variables. Suppose the respective expected values are E(X) = 8 and E(Y) = 3 and the respective variances are V(X) = 9 and V(Y) = 6. Let Z be defined as Z = 2X – 3Y +5. Based on these data, the value of E(Z) is _____ and the value of V(Z) is _____.
Answer: 12 and 90
4. Which of the following statements about the X2 (Chi-square) distribution is always false?
Answer: C
5. Single stage cluster sampling is more precise than simple random sampling when the ___________ is negative.
Answer: intra-cluster correlation
6. In order to estimate the total for a variable of interest, a simple random sample without replacement of size N/4 from a population of size N is sought. After some thought, it is decided that a simple random sample without replacement of size N/2, instead, will be drawn from the same population. By what factor is the variance of this estimate reduced with this increased sample size?
Answer: 3
7. The inverse of the matrix is ___________.
Answer: or
8. The primary goal of principal component analysis is to:
Answer: E
9. A basic question in planning a sample survey is the size of the sample that is required. In your opinion, what factors should be considered when determining the size of the sample and how does each one affect the sample size?
"Statistics Canada is a warm, supportive workplace where you can learn and grow as a data science professional. Through the wide range of projects and available training, there are many opportunities to develop and apply data science and machine learning methods."
"Statistics Canada has a wonderful workplace culture that has allowed me to develop my skills as a methodologist in a dynamic and welcoming atmosphere. The people at Statistics Canada challenge and support each other as producers of quality information, lifelong learners and dedicated professionals."
"At Statistics Canada, my projects allow me to work on many steps of the survey process using state-of-the-art statistical methods. Furthermore, it's easy to achieve a balance between my work and my personal life. I have access to flexible hours which gives me the opportunity to participate in various activities outside of work."
"I've always had varied interests and skills: should I become an author, teacher or mathematician? I discovered that I could do all of that at StatCan, and even more! Not only have I become an expert in implementing the Bootstrap in surveys, but I also provide training on related technical subjects both at the Agency and externally."
"I love my job at Statistics Canada! I have the opportunity to work with really knowledgeable peers on a variety of interesting and innovative projects. And all this while continuing my training in statistics and enjoying a great work life balance!"
"Working at Statistics Canada meets all my needs: I use what I have studied to work directly for the interest of Canadians and society, in a pleasant and healthy work environment. In addition, I can pursue research and international collaboration initiatives, which has allowed me to participate in numerous conferences and to win the International Association for Official Statistics prize for young statisticians in 2020."
"I was glad to find a job that allows me to use all my skills acquired through my years in university. Innovative projects, excellent working conditions, plenty of opportunities for advancement… All this and I get to work with a dynamic group of people! I could not ask for better."
"I enjoy very much the research and development aspects of my daily work that allow me to apply skills learned in university to prominent and innovative projects. Statistics Canada is an employer that also promotes wellness and active living at the workplace. I just love to come to work every day!"
"As a mathematical statistician, I have daily opportunities to work on diverse statistical challenges, both theoretical and applied, alongside many talented individuals. As a working mother, Statistics Canada allows me the flexibility to raise my family while continuing to research new and exciting areas of statistics, all while connecting with statistical colleagues around the world."
"The training offered at Statistics Canada has allowed me to keep learning since my first day on the job — in a wide range of areas including my second language and supervising skills as well as statistics and computer programming."
"The MA stream at Statistics Canada has given me not just a job, but a career. From junior to senior, I have been able to choose my path and work on projects that both interest and challenge me. I feel Statistics Canada has really invested in my development, even while starting a family."
Note that if you require help with the on-line application process, you must contact the Public Service Commission of Canada at 1-888-780-4444.
Contact us at statcan.marecruitment-marecrutement.statcan@statcan.gc.ca.