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Access to Information Request Form

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Office of Privacy Management and Information Coordination
Statistics Canada
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Introduction

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.

Standard classification structure

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.

Standard classification structure of NAPCS Canada 2012

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.

Classification variants

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

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

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.

Status

This standard was approved as a departmental standard on January 16, 2007.

2006 version of the SGC

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.

HTML format

• Archived Information on Standard Geographical Classification (SGC) 2006 Archived
• SGC 2006 - Classification structure

CSV format

• SGC 2006 - Classification structure - CSV version (CSV, 243.61 KB)
• SGC 2006 - Elements - CSV version (CSV, 2 MB)

PDF format

• Archived SGC 2006 - Volume I, The Classification (catalogue no. 12-571-X) Archived

Concordances and documentation on changes

• Archived SGC 2006 - SGC 2001 Archived
• Archived SGC 2001 - SGC 2006 Archived
• Archived Annual changes 2001 - 2006 Archived
• Archived Annual revisions 2001 Archived

Other geographical entities

• Metropolitan areas - SGC 2006
• Economic regions - SGC 2006

Changes to SGC 2006

• Archived Supplement no. 1 Archived
  • Official name for the territory of the Yukon
• Archived Supplement no. 2 Archived
  • Guidelines for Census Metropolitan Area (CMA) name change requests
  • Change of name – Census Metropolitan Area of Abbotsford
• Archived Supplement no. 3 Archived
  • Change of name – Census Metropolitan Area of Kitchener

Reference Maps

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.

• Archived Information on reference maps Archived
• Reference maps

More information

• Archived SGC 2006 - More information Archived

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 estimated^{Note 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 method^{Note 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.

Notes

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.

The work of a Mathematical Statistician

The work of a Mathematical Statistician

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.

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.

Work environment

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.

Training and development

Training and development

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.

In-house courses

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.

Seminars, Conferences, and Publications

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.

University education

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.

Pay rates

Pay rates

Generally speaking, all candidates are hired at the MA -2 level. The starting salary of a mathematical statistician is $67,476^{Footnote *}. 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.

MA pay rates ^{Footnote *}

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
Footnote * Effective October 1st, 2024. Return to footnote * referrer Footnote 1 Mathematical statisticians are called 'methodologists' at Statistics Canada. Return to first footnote 1 referrer Footnote 2 These promotions are based on performance evaluation. Return to first footnote 2 referrer

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.

Who can apply

Who can apply

Persons residing in Canada and Canadian citizens residing abroad. Preference will be given to veterans, Canadian citizens and permanent residents.

Application process

Application process

Step 1: Applications are online through the Public Service Commission website (Government of Canada jobs).

• Provide the following information:
  • your résumé;
  • your grades for courses already taken at a recognized post-secondary institution (with complete codes and titles);
  • a list of courses that you are taking or will be taking at a recognized post-secondary institution during this academic year (with complete codes and titles).

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:

• a copy of your official transcripts from recognized post-secondary institutions;
• proof of Canadian equivalency if you have a foreign degree or degrees.

Qualifications and other requirements

Qualifications and other requirements

Candidates must be able to demonstrate the following:

• A degree from a recognized post-secondary institution with specialization in:
  1. mathematics, statistics or operational research or
  2. one of the physical, life or social sciences, combined with an acceptable number of courses (normally 15 one-term courses/ approximately 45 credits) in mathematics, statistics or operational research at the level of a recognized post-secondary institution.
• Application of mathematical or statistical theories and techniques (including but not limited to probability theory and the distribution of random variables, hypothesis testing, analysis of variance, regression analysis, data analysis).
• Application of mathematical, statistical or survey methods and concepts (including but not limited to questionnaire design, sample design, estimation).
• Demonstrating integrity and respect (acting with transparency and fairness).
• Thinking things through (exercising sound judgment and obtaining relevant facts before making decisions).
• Working effectively with others (understanding their colleagues' roles, responsibilities and workloads, and balancing their own needs with those of other team members).
• Showing initiative and being action-oriented (accepting responsibilities and putting forward ideas and opinions).
• Ability to communicate effectively in writing in English or French.

Frequently asked questions

Frequently asked questions

Visit our Frequently asked questions.

Suggested readings

Suggested readings

In English

• Cochran, W.G. (1977), Sampling Techniques, John Wiley & Sons
• Govindarajulu, Z. (1999), Elements of Sampling Theory and Methods, Prentice Hall
• Hansen, M., Hurwitz, W. and Madow, W. (1953), Sample Survey Methods and Theory, John Wiley and Sons
• Kish, L. (1965), Survey Sampling, John Wiley & Sons
• Levy, P.S. and Lemeshow, S. (1999), Sampling of Populations: Methods and Applications, John Wiley & Sons
• Lohr, S.L. (1999), Sampling : Design and Analysis, Duxbury Press
• Raj, D. and Chandhok, P. (1998), Sample Survey Theory, Narosa Publishing House
• Rao, P.S.R.S. (2000), Sampling Methodologies With Applications, Chapman and Hall
• Sarndal, C.E., Swensson, B. and Wretman, J. (1992), Model-Assisted Survey Sampling, Springer-Verlag
• Satin, A. & Shastry, W. (1993), Survey Sampling: A Non-mathematical guide, 2nd edition, Statistics Canada, catalogue no . 12-602E
• Statistics Canada (2003), Survey methods and practices, Statistics Canada, catalogue no . 12-587-XPE
• Thompson, M.E. (1997), Theory of Sample Surveys, Chapman and Hall
• Thompson, S.K. (1992), Sampling, John Wiley & Sons

In French

• Ardilly, P. (1994), Les techniques de Sondage, Technip
• Brossier, G. et Dussaix, A.-M. éd . (1999), Enquêtes et sondages, Méthodes, modèles, applications, nouvelles approches, Dunod
• Morin, H. (1992) Théorie de l'échantillonnage, Les Presses de l'Université Laval
• Satin, A. & Shastry, W. (1993), L'échantillonnage - Un guide non mathématique, 2ième édition, Statistique Canada, 12-602F au catalogue.
• Statistique Canada (2003), Méthodes et pratiques d'enquête, Statistique Canada, 12-587-XPF au catalogue.
• Tillé, Y. (2001), Théorie des sondages, Échantillonnage et estimation en populations finies, Dunod

Examples of questions for the written test for Mathematical Statisticians

Examples of questions for the written test for Mathematical Statisticians

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.

Part A - Writing Ability

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:

Survey on the lifestyle choices of members of your community

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.

Part B - Knowledge

Probability and Statistics

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?

1. The X² distribution is asymmetrical.
2. The variance of a random variable having a X² distribution is twice its mean.
3. If X₁ and X₂ are two independent random variables with a X² distribution with n₁ and n₂ degrees of freedom respectively, then the variable Y = X₁ + X₂ has an F (Fisher) distribution with n₁ and n₂ degrees of freedom.
4. If X₁ , …, X_n are independent random variables having a normal distribution N(0,1), then X₁² +…+ X_n² has a X² distribution with n degrees of freedom.
5. The X² distribution is dependent only on a single parameter.

Answer: C

Sampling

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

Mathematics

7. The inverse of the matrix $X=5\left[\begin{array}{cc}2& 5\\ 4& -1\end{array}\right]$ is ___________.

Answer: $X^{-1}=\frac{1}{5}\left[\begin{array}{cc}\frac{1}{22}& \frac{5}{22}\\ \frac{2}{11}& \frac{-1}{11}\end{array}\right]$or $X^{-1}=\left[\begin{array}{cc}\frac{1}{110}& \frac{1}{22}\\ \frac{2}{55}& \frac{-1}{55}\end{array}\right]$

Data Analysis

8. The primary goal of principal component analysis is to:

1. Divide a set of multivariate observations into classes.
2. Assign a particular multivariate observation to one of several classes.
3. Characterize the correlation structure between two sets of variables by replacing them by two smaller sets of variables which are highly correlated.
4. Find the variables among a set of predictor variables that are the best predictors of a set of variables of interest.
5. Explain the variability in a large set of variables by replacing it by a smaller set of transformed variables that explains a large portion of the total variability.

Answer: E

Open Question

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?

Testimonials

Testimonials

"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!"

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Integrated Business Statistics Program (IBSP)

Reporting Guide

This guide is designed to assist you as you complete the Annual Capital Expenditures Survey

Preliminary Estimate for 2014 and Intentions for 2015. If you need more information, please call the Statistics Canada Help Line at the number below.

Help Line: 1-877-604-7828 or 1-800-972-9692

Your answers are confidential.

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act.

Table of contents

Data-sharing agreements
Record linkages
Reporting period information
Definition
Industry characteristics

Data sharing Agreements

To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

 

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, and the Yukon.

The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician and returning it with the completed questionnaire. Please specify the organizations with which you do not want to share your data.

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut as well as Natural Resources Canada and Environment Canada.

For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Record linkages

To enhance the data from this survey, Statistics Canada may combine it with information from other surveys or from administrative sources.

Reporting period information

For the purpose of this survey, please report information for your 12 month fiscal period for which the Final day occurs on or between April 1, 2014 - March 31, 2015 for 2014 and April 1, 2015 - March 31, 2016 for 2015.

May 2013 - April 2014 (04/14)
June 2013 - May 2014 (05/14)
July 2013 - June 2014 (06/14)
Aug. 2013 - July 2014 (07/114)
Sept. 2013 - Aug. 2014 (08/14)
Oct. 2013 - Sept. 2014 (09/14)
Nov. 2013 - Oct. 2014 (10/14)
Dec. 2013 - Nov. 2014 (11/14)
Jan. 2014 - Dec. 2014 (12/14)
Feb. 2014 - Jan. 2015 (01/15)
March 2014 - Feb. 2015 (02/15)
April 2014 - March 2015 (03/15)

Here are other examples of fiscal periods that fall within the required dates:

• September 18, 2014 to September 15, 2015 (e.g., floating year-end)
• June 1, 2014 to December 31, 2015 (e.g., a newly opened business)

Definitions

What are Capital Expenditures?

Capital Expenditures are the gross expenditures on fixed assets for use in the operations of your organization or for lease or rent to others.

Include:

• Cost of all new buildings, engineering, machinery and equipment which normally have a life of more than one year and are charged to fixed asset accounts
• Modifications, acquisitions and major renovations
• Capital costs such as feasibility studies, architectural, legal, installation and engineering fees
• Subsidies
• Capitalized interest charges on loans with which capital projects are financed
• Work done by own labour force
• Additions to work in progress

How to Treat Leases

Include:

• assets acquired as a lessee through either a capital or financial lease;
• assets acquired for lease to others as an operating lease.

Exclude

• assets acquired for lease to others, either as a capital or financial lease.

Information for Government Departments

The following applies to government departments only:

Include

• all capital expenditures without taking into account the capitalization threshold of your department;
• Grants and/or subsidies to outside entities (e.g., municipalities, agencies, institutions or businesses) are not to be included;
• Departments are requested to exclude from reported figures budgetary items pertaining to any departmental agency and proprietary crown corporation as they are surveyed separately;
• Federal departments are to report expenditures paid for by the department, regardless of which department awarded the contract;
• Provincial departments are to include any capital expenditures on construction (exclude outlays for land) or machinery and equipment, for use in Canada, financed from revolving funds, loans attached to revolving funds, other loans, the Consolidated Revenue Fund or special accounts.

Industry characteristics

Report the value of the projects expected to be put in place during the year. Include the gross expenditures (including subsidies) on fixed assets for use in the operations of your organization or for lease or rent to others. Include all capital costs such as feasibility studies, architectural, legal, installation and engineering fees as well as work done by your own labour force.

New Assets, Renovation, Retrofit (Column 1), includes both existing assets being upgraded and acquisitions of new assets

The following explanations are Not applicable to government departments:

• include - Capitalized interest charges on loans with which capital projects are financed
• exclude - If you are capitalizing your leased fixed assets as a lessee in accordance with the Canadian Institute of Chartered Accountants’ recommendations, please exclude the total of the capitalization of such leases during the year from capital expenditures

Purchase of Used Canadian Assets (Column 2)

Definition: Used fixed assets may be defined as existing buildings, structures or machinery and equipment which have been previously used by another organization in Canada that you have acquired during the time period being reported on this questionnaire.

Explanation: The objective of our survey is to measure gross annual new acquisitions to fixed assets separately from the acquisition of gross annual used fixed assets in the Canadian economy as a whole.

Hence, the acquisition of a used fixed Canadian asset should be reported separately since such acquisitions would not change the aggregates of our domestic inventory of fixed assets, it would simply mean a transfer of assets within Canada from one organization to another.

Imports of used assets, on the other hand, should be included with the new assets (Column 1) because they are newly acquired for the Canadian economy.

Work in Progress:
Work in progress represents accumulated costs since the start of capital projects which are intended to be capitalized upon completion.

Typically capital investment includes any expenditure on an asset in which its’ life is greater than one year. Capital items charged to operating expenses are defined as expenditures which could have been capitalized as part of the fixed assets, but for various reasons, have been charged to current expenses.

Land
Capital expenditures for land should include all costs associated with the purchase of the land that are not amortized or depreciated.

Residential Construction
Report the value of residential structures including the housing portion of multi-purpose projects and of townsites with the following Exceptions:

• buildings that have accommodation units without self-contained or exclusive use of bathroom and kitchen facilities (e.g., some student and senior citizen residences)
• the non-residential portion of multi-purpose projects and of townsites
• associated expenditures on services

The exceptions should be included in the appropriate construction (e.g., non-residential) asset.

Non-Residential Building Construction (excluding land purchase and residential construction)
Report the total cost incurred during the year of building and engineering construction (contract and by own employees) whether for your own use or rent to others. Include also:

• the cost of demolition of buildings, land servicing and of site-preparation
• leasehold and land improvements
• townsite facilities, such as streets, sewers, stores, schools

Non-residential engineering construction

Report the total cost incurred during the year of engineering construction (contract and by own employees) whether for your own use or rent to others. Include also:

• the cost of demolition of buildings, land servicing and of site-preparation
• oil or gas pipelines, including pipe and installation costs
• all preconstruction planning and design costs such as engineer and consulting fees and any materials supplied to construction contractors for installation, etc.
• communication engineering, including transmission support structures, cables and lines, etc.
• electric power engineering, including wind and solar plants, nuclear production plants, power distribution networks, etc.

Machinery and Equipment
Report total cost incurred during the year of all new machinery, whether for your own use or for lease or rent to others. Any capitalized tooling should also be included. Include progress payments paid out before delivery in the year in which such payments are made. Receipts from the sale of your own fixed assets or allowance for scrap or trade-in should not be deducted from your total capital expenditures. Any balance owing or holdbacks should be reported in the year the cost is incurred.

Include:

• automobiles, trucks, professional and scientific equipment, office and store furniture and appliances
• computers (hardware and software), broadcasting, telecommunication and other information and communication technology equipment
• motors, generators, transformers
• any capitalized tooling expenses
• progress payments paid out before delivery in the year in which such payments are made
• any balance owing or holdbacks should be reported in the year the cost is incurred

Software

Capital expenditures for software should include all costs associated with the purchase of software.

Include:

• Pre-packaged software
• Custom software developed in-house/own account
• Custom software design and development, contracted out

Research and Development

Research and development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications. Basic and applied research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomenon and observable facts. Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, installing new process, systems and services, or improving substantially those already produced or installed.

Capacity Utilization (Manufacturing Companies only)

Capacity use (utilization) is calculated by taking the actual production level for an establishment (production can be measured in dollars or units) and dividing it by the establishment’s capacity production level.

Capacity production is defined as maximum production attainable under normal conditions.

To calculate capacity production, follow the establishment’s operating practices with respect to the use of productive facilities, overtime, workshifts, holidays, etc. For example, if your plant normally operates with one shift of eight hours a day five days a week then capacity will be calculated subject to these conditions and not on the hypothetical case of three shifts a day, seven days a week.

Example:
Plant “A” normally operates one shift a day, five days a week and given this operating pattern capacity production is 150 units of product “A” for the month. In that month actual production of product “A” was 125 units. The capacity utilization rate for plant “A” is (125/150) * 100 = 83%

Now suppose that plant “A” had to open a shift on Saturdays to satisfy an abnormal surge in demand for product “A”. Given this plant’s normal operating schedule, capacity production remains at 150 units. Actual production hasgrown to 160 units, so capacity utilization would be (160/150) * 100 = 107%.