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Statistics, Faculty of Science

STAT_V: Statistics

Introductory courses in probability and statistics are offered by many different departments at UBC. For a list of these courses and details concerning restrictions on the number of credits students may obtain for such courses, see "Pairing Lists" and "Probability and Statistics" in the Science section. The following course is for students in the Faculty of Applied Science: STAT_V 251. Additional fees are charged for some courses.

  1. STAT_V 200 (3) Elementary Statistics for Applications

    Classical, nonparametric, and robust inferences about means, variances, and analysis of variance, using computers. Emphasis on problem formulation, assumptions, and interpretation. See the Faculty of Science Credit Exclusion Lists: https://vancouver.calendar.ubc.ca/faculties-colleges-and-schools/facult… [3-1-0] Prerequisite: One of any course on the MATH_V 100 credit exclusion list [https://vancouver.calendar.ubc.ca/faculties-colleges-and-schools/facult…], MATH_V 190, SCIE_V 001, MATH_O 100.

  2. STAT_V 201 (3) Statistical Inference for Data Science

    Classical and simulation-based techniques for estimation and hypothesis testing, including inference for means and proportions. Emphasis on case studies and real data sets, as well as reproducible and transparent workflows when writing computer scripts for analysis and reports. [3-0-1] Prerequisite: DSCI 100.

  3. STAT_V 203 (3) Statistical Methods

    Organizing, displaying and summarizing data. Inference estimation and testing for elementary probability models. Not for credit towards a B.Sc. (Consult the Credit Exclusion list within the Faculty of Science section in the Calendar.) [3-1-0] Prerequisite: MATH 11. Or Pre-calculus 11.

  4. STAT_V 251 (3) Introductory Probability and Statistics

    Probability, discrete and continuous random variables, joint probability distributions, estimation, hypothesis testing, regression, analysis of variance. (Consult the Credit Exclusion list within the Faculty of Science section of the Calendar). [3-1-0] Prerequisite: One of APSC_V 173, MATH_V 101, MATH_V 103, MATH_V 105, MATH_V 121, SCIE_V 001, MATH_O 101, MATH_O 103.

  5. STAT_V 300 (3) Intermediate Statistics for Applications

    Further topics in statistical inference, including parametric and non-parametric methods, goodness-of-fit methods, analysis of variance and covariance, regression analysis, categorical data analysis, experimental designs, time series, model fitting, and statistical computing. [3-1-0] Prerequisite: One of STAT_V 200, STAT_V 203, STAT_V 241, STAT_V 251, BIOL_V 300, BUSI_V 291, COMM_V 191, COMM_V 291, ECON_V 325, ECON_V 327, FRST_V 231, KIN_V 206, LFS_V 252, POLI_V 380, PSYC_V 218, PSYC_V 278, HES_O 340, POLI_O 400, STAT_O 121, STAT_O 121, STAT_O 205, STAT_O 230. Equivalency: COMM_V 411.

  6. STAT_V 301 (3) Statistical Modelling for Data Science

    Data analysis using statistical models and algorithms (e.g., linear and logistic regression, peeking, bandit, and variable selection algorithms) in case studies from different disciplines. Generative versus out-of-sample predictive models. Reproducible and transparent workflows for computer scripts and reports. [3-0-1] Prerequisite: STAT_V 201 and one of MATH_V 100, MATH_V 102, MATH_V 104, MATH_V 110, MATH_V 120, MATH_V 180, MATH_V 184, SCIE_V 001, MATH_O 100.

  7. STAT_V 302 (3) Introduction to Probability

    Basic notions of probability, random variables, expectation and conditional expectation, limit theorems. (Consult the Credit Exclusion list within the Faculty of Science section in the Calendar.) [3-0-0] Prerequisite: One of MATH_V 200, MATH_V 226, MATH_V 217, MATH_V 253, MATH_V 254, MATH_O 200. Equivalency: MATH_V 302.

  8. STAT_V 303 (3) Introduction to Probabilistic Modelling and Simulation

    Core concepts in probability taught through probabilistic generative modelling, simulation of random variables, basic Monte Carlo, common probability distributions and models, basic probabilistic model design and inference. [3-0-0] Prerequisites: All of (a) one of MATH_V 101, MATH_V 103, MATH_V 105, MATH_V 121, SCIE_V 001, (b) one of CPSC_V 103, CPSC_V 110, and (c) one of DSCI_V 100, ECON_V 325, STAT_V 200, STAT_V 251.

  9. STAT_V 305 (3) Introduction to Statistical Inference

    Review of probability theory. Sampling distribution theory, large sample theory and methods of estimation and hypothesis testing, including maximum likelihood estimation, likelihood ratio testing and confidence interval construction. [3-0-1] Prerequisite: Either (a) one of STAT_V 200, STAT_V 203, BIOL_V 300, STAT_V 251, COMM_V 291, ECON_V 325, FRST_V 231, PSYC_V 218, PSYC_V 366, PSYO_O 271, STAT_O 121, STAT_O 124, STAT_O 205, STAT_O 230 and one of MATH_V 302, STAT_V 302, STAT_O 203; or (b) DSCI_V 200 and STAT_V 201 and one of MATH_V 302, STAT_V 302, STAT_O 203; or (c) a score of 65% or higher in one of MATH_V 302, STAT_V 302, STAT_O 203. The Department recommends that students meet the prerequisite through option (a) or (b).

  10. STAT_V 306 (3) Finding Relationships in Data

    Modelling a response (output) variable as a function of several explanatory (input) variables: multiple regression for a continuous response, logistic regression for a binary response, and log-linear models for count data. Finding low-dimensional structure: principal components analysis. Cluster analysis. (Consult the Credit Exclusion List within the Faculty of Science section in the Calendar). [3-0-1] Prerequisite: One of MATH_V 152, MATH_V 221, MATH_V 223, MATH_O 222 and one of STAT_V 200, STAT_V 241, STAT_V 251, STAT_V 300, BIOL_V 300, BUSI_V 291, COMM_V 191, COMM_V 291, ECON_V 325, ECON_V 327, FRST_V 231, PSYC_V 218, PSYC_V 278, PSYO_O 271, STAT_O 124, STAT_O 205, STAT_O 230 and one of MATH_V 302, STAT_V 302, STAT_O 203.

  11. STAT_V 321 (4) Stochastic Signals and Systems

    Stochastic behaviour of signals and systems (e.g., communication systems); discrete and continuous probability; random processes; modelling and identification of linear time-invariant systems; binary hypothesis testing and decision making. [3-0-2] Prerequisite: One of ELEC 221, STAT 305. STAT 305 may be taken concurrently, with registration assistance from the Student Programs Coordinator in the Department of Statistics. Equivalency: ELEC321

  12. STAT_V 335 (3) Statistics in Quality Assurance

    Philosophy of quality improvement and total quality control. Definitions of quality. Deming's principles, Ishikawa's tools, control charts, acceptance sampling, continuous improvement, quality design. Credit cannot be obtained for both STAT 335 and WOOD 335. [3-0-1] Prerequisite: One of STAT 200, STAT 241, STAT 251, BIOL 300.

  13. STAT_V 344 (3) Sample Surveys

    Planning and practice of sample surveys. Random sampling, bias and variance, unequal probability sampling, systematic, multistage and stratified sampling, ratio and regression estimators, post-stratification, establishing a frame, pretesting, pilot studies, nonresponse and additional topics. [3-0-1] Prerequisite: One of STAT 200, STAT 241, STAT 251, BIOL 300, COMM 291, ECON 325, ECON 327, FRST 231, PSYC 218, PSYC 278, PSYC 366. Corequisite: One of MATH 302, STAT 302.

  14. STAT_V 398 (3) Co-operative Work Placement I

    Work experience in an industrial research setting. Normally taken during Winter Session of third year. Restricted to students admitted to the Co-operative Education Program in Statistics. Prerequisite: Registration in Statistics Honours or Major Program. This course is not eligible for Credit/D/Fail grading.

  15. STAT_V 399 (3) Co-operative Work Placement II

    Work experience in an industrial research setting. Normally taken during Summer Session following third year. Restricted to students admitted to the Co-operative Education Program in Statistics. Prerequisite: STAT_V 398. This course is not eligible for Credit/D/Fail grading.

  16. STAT_V 404 (3) Design and Analysis of Experiments

    Theory and application of analysis of variance for standard experimental designs, including blocked, nested, factorial and split plot designs. Fixed and random effects, multiple comparisons, analysis of covariance. (Consult the Credit Exclusion list within the Faculty of Science section in the Calendar.) [3-0-1] Prerequisite: STAT_V 305, one of MATH_V 152, MATH_V 221, or MATH_V 223. Corequisite: ECON_V 326 or STAT_V 306.

  17. STAT_V 405 (3) Bayesian Statistics

    Bayesian approaches to statistical inference: probabilistic modelling, Bayesian inference workflows, approximation of posterior distributions supported by modelling languages, analysis of Bayesian procedures and posterior approximation methods. [3-0-1] Prerequisites: One of MATH_V 302, STAT_V 302, or MATH_V 418, and either STAT_V 305 or STAT_V 460.

  18. STAT_V 406 (3) Methods for Statistical Learning

    Flexible, data-adaptive methods for regression and classification models; regression smoothers; penalty methods; assessing accuracy of prediction; model selection; robustness; classification and regression trees; nearest-neighbour methods; neural networks; model averaging and ensembles; computational time and visualization for large data sets. [3-0-1] Prerequisite: a) One of STAT_V 306, CPSC_V 340, or b) STAT_V 301 and one of MATH_V 152, MATH_V 221, MATH_V 223 and one of MATH_V 302, STAT_V 302.

  19. STAT_V 416 (3) Advanced Linear Modelling

    Advanced topics in linear modelling, including weighted least squares, model selection, robust methods, splines, Bayesian methods, mixed effects models, and generalized linear models. [3-0-1] Prerequisites: Both STAT_V 305 and STAT_V 306.

  20. STAT_V 443 (3) Time Series and Forecasting

    Trend and seasonality, autocorrelation, stationarity, stochastic models, exponential smoothing, Holt-Winters methods, Box-Jenkins approach, frequency domain analysis. [3-0-1] Prerequisite: Both (a) one of MATH_V 302, MATH_V 318, STAT_V 302 and (b) one of ECON_V 326, ECON_V 328, STAT_V 300, STAT_V 301, STAT_V 306. Corequisite: STAT_V 305.

  21. STAT_V 445 (3) Introduction to Exploratory Data Analysis

    Methods for exploring and presenting the structure of data: one group of numbers, several groups, bivariate data, time series data and two-way tables. Data displays, outlier identification, transformations, resistant regression, several types of data smoothing, comparisons with standard statistical methods. [3-0-1] Prerequisite: [STAT306]

  22. STAT_V 447 (2-6) Special Topics in Statistics

    Students should consult the Statistics Department for the particular topics offered in a given year. Prerequisite: STAT_V 305.

  23. STAT_V 449 (3-6) Statistics Honours Project

    A research project, undertaken under the supervision of a faculty member, resulting in a written report. Prerequisite: Open to students enrolled in a Statistics Honours specialization and at class standing 4. Permission of the Undergraduate Advisor and supervising faculty member is required.

  24. STAT_V 450 (3) Case Studies in Statistics

    Readings and projects in areas of current statistical application including environmental science, industrial statistics, official statistics, actuarial statistics, and medical statistics. [3-0-1] Prerequisite: [STAT306]

  25. STAT_V 460 (3) Statistical Inference I

    Statistical models and their properties, estimation methods, properties of point and interval estimation, likelihood, Bayesian inference. Intended for Honours students. [3-0-0] Prerequisite: All of MATH 320, STAT 305 and one of MATH 152, MATH 221, MATH 223.

  26. STAT_V 461 (3) Statistical Inference II

    Hypothesis testing and model selection in modern statistics, confidence regions, multiple testing, model comparison criteria. Intended for Honours students. [3-0-0] Prerequisite: [STAT460]

  27. STAT_V 498 (3) Co-operative Work Placement III

    Work experience in an industrial research setting. Normally taken during Summer Session following fourth year. Restricted to students admitted to the Co-operative Education Program in Statistics. This course is not eligible for Credit/D/Fail grading.

  28. STAT_V 499 (3) Co-operative Work Placement IV

    Work experience in an industrial research setting. Normally taken during Term 1 of Winter Session of fifth year. Restricted to students admitted to the Co-operative Education Program in Statistics. This course is not eligible for Credit/D/Fail grading.

  29. STAT_V 518 (3) Theoretical Statistics

    This course is not eligible for Credit/D/Fail grading.

  30. STAT_V 520 (1-6) Topics in Bayesian Analysis and Decision Theory

    This course is not eligible for Credit/D/Fail grading.

  31. STAT_V 521 (1-6) Topics in Multivariate Analysis

    This course is not eligible for Credit/D/Fail grading.

  32. STAT_V 522 (1-6) Topics in Asymptotic Theory and Statistical Inference

    This course is not eligible for Credit/D/Fail grading.

  33. STAT_V 526 (1-6) Topics in Smoothing Methods

    This course is not eligible for Credit/D/Fail grading.

  34. STAT_V 527 (1-6) Topics in Biostatistics

    This course is not eligible for Credit/D/Fail grading.

  35. STAT_V 530 (1-3) Bayesian Inference and Decision

    This course is not eligible for Credit/D/Fail grading.

  36. STAT_V 533 (1-3) Survival Analysis

    This course is not eligible for Credit/D/Fail grading.

  37. STAT_V 535 (1-3) Statistical Computing

    This course is not eligible for Credit/D/Fail grading.

  38. STAT_V 536 (1-3) Statistical Theory for the Design and Analysis of Clinical Studies

    This course is not eligible for Credit/D/Fail grading.

  39. STAT_V 538 (1-3) Generalized Linear Models

    This course is not eligible for Credit/D/Fail grading.

  40. STAT_V 540 (1-3) Statistical Methods for High Dimensional Biology

    Equivalency: BIOF_V 540, GSAT_V 540. This course is not eligible for Credit/D/Fail grading.

  41. STAT_V 541 (1-3) Applied Multivariate Analysis

    This course is not eligible for Credit/D/Fail grading.

  42. STAT_V 543 (1-3) Time Series Analysis

    This course is not eligible for Credit/D/Fail grading.

  43. STAT_V 545 (1-3) Exploratory Data Analysis

    This course is not eligible for Credit/D/Fail grading.

  44. STAT_V 547 (1-6) Topics in Statistics

    Students should consult the Statistics Department for the particular advanced topics offered in a given year. This course is not eligible for Credit/D/Fail grading.

  45. STAT_V 548 (1-6) Directed Studies in Statistics

    This course is not eligible for Credit/D/Fail grading.

  46. STAT_V 549 (6-12) Thesis for Master's Degree

    This course is not eligible for Credit/D/Fail grading.

  47. STAT_V 550 (3) Techniques of Statistical Consulting

    This course is not eligible for Credit/D/Fail grading.

  48. STAT_V 551 (3) Statistical Consulting Practicum

    This course is not eligible for Credit/D/Fail grading.

  49. STAT_V 560 (3) Statistical Theory I

    Credit will not be given for both STAT_V 460 and STAT_V 560. [3-0-0]. This course is not eligible for Credit/D/Fail grading.

  50. STAT_V 561 (3) Statistical Theory II

    This course is not eligible for Credit/D/Fail grading.

  51. STAT_V 589 (3) M.Sc. Project

    This course is not eligible for Credit/D/Fail grading.

  52. STAT_V 598 (3) Co-operative Work Placement I

    Restricted to students admitted to the Co-operative M.Sc. Education Program in Statistics. This course is not eligible for Credit/D/Fail grading.

  53. STAT_V 599 (3) Co-operative Work Placement II

    Restricted to students admitted to the Co-operative M.Sc. Education Program in Statistics. Prerequisite: STAT_V 598. This course is not eligible for Credit/D/Fail grading.

  54. STAT_V 649 (0) Doctoral Dissertation

    This course is not eligible for Credit/D/Fail grading.

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