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Department of Statistics and Actuarial Science | Faculty of Science Simon Fraser University Calendar | Summer 2024

Statistics Minor

Admission Requirements

Students may be admitted to the Statistics minor program by application to the Department of Statistics after admission to SFU. See admission requirements.

Courses for Further Credit

Once a STAT course has been completed with a grade of C- or higher, STAT courses that are prerequisites to this course may not be taken for further credit without permission of the department.

Prerequisite Grade Requirement

Students must have a grade of C- or better in prerequisites for STAT courses.

Graduation Grade Point Averages

See required GPA for graduation from the Statistics minor program.

Program Requirements

Lower Division Requirements

Students complete one of

MATH 150 - Calculus I with Review (4)

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Topics as for Math 151 with a more extensive review of functions, their properties and their graphs. Recommended for students with no previous knowledge of Calculus. In addition to regularly scheduled lectures, students enrolled in this course are encouraged to come for assistance to the Calculus Workshop (Burnaby), or Math Open Lab (Surrey). Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B+, or MATH 100 with a grade of at least B-, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 151, 154 or 157 may not take MATH 150 for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Mahsa Faizrahnemoon
May 6 – Aug 2, 2024: Mon, Wed, Fri, 1:30–2:20 p.m.
Burnaby
D101 May 6 – Aug 2, 2024: Tue, 8:30–9:20 a.m.
Burnaby
D102 May 6 – Aug 2, 2024: Tue, 9:30–10:20 a.m.
Burnaby
D103 May 6 – Aug 2, 2024: Tue, 10:30–11:20 a.m.
Burnaby
OP01 TBD
MATH 151 - Calculus I (3)

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.

MATH 154 - Mathematics for the Life Sciences I (3)

Designed for students specializing in the life sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications, integration, and differential equations; mathematical models of biological processes and their implementation and analysis using software. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C-, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.

MATH 157 - Calculus I for the Social Sciences (3)

Designed for students specializing in business or the social sciences. Topics include: limits, growth rate and the derivative; logarithmic, exponential and trigonometric functions and their application to business, economics, optimization and approximation methods; introduction to functions of several variables with emphasis on partial derivatives and extrema. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 154 may not take MATH 157 for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Paul Tupper
May 6 – Aug 2, 2024: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
OP01 TBD

and one of

MATH 152 - Calculus II (3)

Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. First-order separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151, with a minimum grade of C-; or MATH 154 or 157 with a grade of at least B. Students with credit for MATH 155 or 158 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Stephen Choi
May 6 – Aug 2, 2024: Mon, Wed, Fri, 8:30–9:20 a.m.
Burnaby
OP01 TBD
MATH 155 - Mathematics for the Life Sciences II (3)

Designed for students specializing in the life sciences. Topics include: vectors and matrices, partial derivatives, multi-dimensional integrals, systems of differential equations, compartment models, graphs and networks, and their applications to the life sciences; mathematical models of multi-component biological processes and their implementation and analysis using software. Prerequisite: MATH 150, 151 or 154, with a minimum grade of C-; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Veselin Jungic
May 6 – Aug 2, 2024: Mon, Wed, Fri, 8:30–9:20 a.m.
Burnaby
OPO1 TBD
MATH 158 - Calculus II for the Social Sciences (3)

Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special first-order equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.

MATH 232 - Applied Linear Algebra (3)

Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Justin Chan
May 6 – Aug 2, 2024: Mon, Wed, Fri, 1:30–2:20 p.m.
Surrey
OP01 TBD
MATH 240 - Algebra I: Linear Algebra (3)

Linear equations, matrices, determinants. Real and abstract vector spaces, subspaces and linear transformations; basis and change of basis. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. Applications. Subject is presented with an abstract emphasis and includes proofs of the basic theorems. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 232 cannot take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Imin Chen
May 6 – Aug 2, 2024: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
D101 May 6 – Aug 2, 2024: Thu, 9:30–10:20 a.m.
Burnaby
D102 May 6 – Aug 2, 2024: Thu, 2:30–3:20 p.m.
Burnaby
D103 May 6 – Aug 2, 2024: Thu, 3:30–4:20 p.m.
Burnaby

and one of

BUS 232 - Business Statistics (3)

An introduction to business statistics (descriptive and inferential statistics) with a heavy emphasis on applications and the use of EXCEL. Students will be required to use statistical applications to solve business problems. Corequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. Students with credit for BUEC 232 or ECON 233 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Negar Ganjouhaghighi
May 6 – Aug 2, 2024: Wed, 2:30–5:20 p.m.
Burnaby
D200 Negar Ganjouhaghighi
May 6 – Aug 2, 2024: Thu, 2:30–5:20 p.m.
Surrey
OP01 May 6 – Aug 2, 2024: Wed, 9:30 a.m.–2:20 p.m.
Burnaby
OP02 May 6 – Aug 2, 2024: Thu, 9:30 a.m.–2:20 p.m.
Burnaby
OP05 May 6 – Aug 2, 2024: Thu, 12:30–2:20 p.m.
Surrey
OP06 May 6 – Aug 2, 2024: Fri, 12:30–2:20 p.m.
Surrey
ECON 233 - Introduction to Economic Data and Statistics (3)

Introduces statistical methods, concepts and their application to economic data using both spreadsheets (e.g., Excel) and a specialized statistical programming language such as R. Prerequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. MATH 150, MATH 151, MATH 154, or MATH 157 may be taken concurrently with ECON 233. Students who have taken ECON 333 first may not then take this course for further credit. STAT 270 or BUS 232 will be accepted in lieu of this course.

STAT 201 - Statistics for the Life Sciences (3)

Research methodology and associated statistical analysis techniques for students with training in the life sciences. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 201 if they already have credit for - or are taking concurrently - STAT 101, 203, 205, 285, or any upper division STAT course. Quantitative.

Section Instructor Day/Time Location
D100 Brad McNeney
May 6 – Aug 2, 2024: Wed, 1:30–2:20 p.m.
May 6 – Aug 2, 2024: Fri, 12:30–2:20 p.m.
Burnaby
Burnaby
OL01 Wei Lin
Online
OP01 TBD
STAT 203 - Introduction to Statistics for the Social Sciences (3)

Descriptive and inferential statistics aimed at students in the social sciences. Scales of measurement. Descriptive statistics. Measures of association. Hypothesis tests and confidence intervals. Students in Sociology and Anthropology are expected to take SA 255 before this course. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units including a research methods course such as SA 255, CRIM 220, POL 200W, or equivalent. Students cannot obtain credit for STAT 203 if they already have credit for - or are taking concurrently - STAT 101, 201, 205, 285, or any upper division STAT course. Quantitative.

Section Instructor Day/Time Location
E100 Scott Pai
May 6 – Aug 2, 2024: Tue, 4:30–5:20 p.m.
May 6 – Aug 2, 2024: Thu, 4:30–6:20 p.m.
Burnaby
Burnaby
OL01 Wei Lin
Online
OP01 TBD
STAT 205 - Introduction to Statistics (3)

The collection, description, analysis and summary of data, including the concepts of frequency distribution, parameter estimation and hypothesis testing. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 205 if they already have credit for - or are taking concurrently - STAT 101, 201, 203, 285, or any upper division STAT course. Quantitative.

STAT 270 - Introduction to Probability and Statistics (3)

Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158, with a minimum grade of C-. Students wishing an intuitive appreciation of a broad range of statistical strategies may wish to take STAT 100 first. Quantitative.

Section Instructor Day/Time Location
D100 Sessional
May 6 – Aug 2, 2024: Wed, 11:30 a.m.–12:20 p.m.
May 6 – Aug 2, 2024: Fri, 10:30 a.m.–12:20 p.m.
Burnaby
Burnaby
OL01 Gamage Perera
Online
OP01 TBD

and all of

STAT 260 - Introductory R for Data Science (2)

An introduction to the R programming language for data science. Exploring data: visualization, transformation and summaries. Data wrangling: reading, tidying, and data types. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 261. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

STAT 261 - Laboratory for Introductory R for Data Science (1)

A hands-on application of the R programming language for data science. Using the R concepts covered in STAT 260, students will explore (visualize, transform, and summarize) and wrangle (read and tidy) data. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 260. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

Upper Division Requirements

Students complete a total of 15 units, including exactly one of

ECON 333 - Statistical Analysis of Economic Data (4)

An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Prerequisite: ECON 103 with a minimum grade of C- or ECON 113 with a minimum grade of A-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of A-; ECON 233 or BUS (or BUEC) 232 or STAT 270, MATH 157, all with a minimum grade of C-; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232 or STAT 270 grade must contact the undergraduate advisor in economics. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Vasyl Golovetskyy
May 6 – Aug 2, 2024: Tue, 2:30–4:20 p.m.
May 6 – Aug 2, 2024: Thu, 2:30–3:20 p.m.
Burnaby
Burnaby
D101 May 6 – Aug 2, 2024: Wed, 9:30–10:20 a.m.
Burnaby
D102 May 6 – Aug 2, 2024: Wed, 10:30–11:20 a.m.
Burnaby
D103 May 6 – Aug 2, 2024: Wed, 11:30 a.m.–12:20 p.m.
Burnaby
D104 May 6 – Aug 2, 2024: Thu, 12:30–1:20 p.m.
Burnaby
D105 May 6 – Aug 2, 2024: Thu, 1:30–2:20 p.m.
Burnaby
D106 May 6 – Aug 2, 2024: Thu, 9:30–10:20 a.m.
Burnaby
STAT 302 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take the course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Brad McNeney
May 6 – Aug 2, 2024: Mon, 2:30–3:20 p.m.
May 6 – Aug 2, 2024: Thu, 2:30–4:20 p.m.
Burnaby
Burnaby
OL01 Gamage Perera
Online
OP01 TBD
STAT 305 - Introduction to Biostatistical Methods for Health Sciences (3)

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take this course for further credit. Quantitative.

STAT 350 - Linear Models in Applied Statistics (3)

Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

A minimum of 11 of the 15 upper division units must be completed using STAT courses other than STAT 310, STAT 311, and STAT 320. The remaining 4 units may be substituted with upper division non-STAT units that focus on statistical inference, study design, or quantitative reasoning, such as BUS 336. The eligibility of other non-STAT courses will be at the discretion of departmental advisors. Recommended STAT courses are listed below.

STAT 342 - Introduction to Statistical Computing and Exploratory Data Analysis - SAS (2)

Introduces the SAS statistical package. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333, with a minimum grade of C-. Students with credit for STAT 340 may not take STAT 342 for further credit.

STAT 360 - Advanced R for Data Science (2)

Advanced R programming methods for data science. Tools for reproducible research. Version control. Data structures, subsetting, functions, environments, and debugging. Functional programming. Code performance: profiling, memory, integrating R and C++. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 361.

STAT 361 - Laboratory for Advanced R for Data Science (1)

A hands-on application of advanced R programming methods for data science. Using the R concepts covered in STAT 360 and tools for reproducible research, students will work with different data structures, write functions, and debug and optimize the performance of their code. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 360.

STAT 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or ECON 333, all with a minimum grade of C-. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. Quantitative.

STAT 445 - Applied Multivariate Analysis (3)

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 452 - Statistical Learning and Prediction (3)

An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 475 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for the former STAT 402 or 602 may not take this course for further credit. Quantitative.

STAT 485 - Applied Time Series Analysis (3)

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. This course may not be taken for further credit by students who have credit for ECON 484. Quantitative.

Faculty Requirements

Statistics minor candidates are subject to the general regulations of the faculty in which they are enrolled.

Writing, Quantitative, and Breadth Requirements

Students admitted to Simon Fraser University beginning in the fall 2006 term must meet writing, quantitative and breadth requirements as part of any degree program they may undertake. See Writing, Quantitative, and Breadth Requirements for university-wide information.

WQB Graduation Requirements

A grade of C- or better is required to earn W, Q or B credit

Requirement

Units

Notes
W - Writing

6

Must include at least one upper division course, taken at Simon Fraser University within the student's major subject; two courses (minimum three units each)

Q - Quantitative

6

Q courses may be lower or upper division; two courses (total six units or more)
B - Breadth

18

Designated Breadth

Must be outside the student's major subject, and may be lower or upper division:

Two courses (total six units or more) Social Sciences: B-Soc
Two courses (total six units or more) Humanities: B-Hum
Two courses (total six units or more) Sciences: B-Sci

6

Additional Breadth

Two courses (total six units or more) outside the student's major subject (may or may not be B-designated courses, and will likely help fulfil individual degree program requirements).

Students choosing to complete a joint major, joint honours, double major, two extended minors, an extended minor and a minor, or two minors may satisfy the breadth requirements (designated or not designated) with courses completed in either one or both program areas.

Residency Requirements and Transfer Credit

  • At least half of the program's total units must be earned through Simon Fraser University study.
  • At least two thirds of the program's total upper division units must be earned through Simon Fraser University study.