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 251. Additional fees are charged for some courses."
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 MATH 101, MATH 103, MATH 105, MATH 121, SCIE 001.
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.
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.
Probability models, random variables and vectors, estimation, testing, regression, analysis of variance, goodness of fit, quality control. (Consult the Credit Exclusion list within the Faculty of Science section of the Calendar). [3-1-0]
Prerequisite: One of MATH 101, MATH 103, MATH 105, MATH 121, SCIE 001.
Probability, discrete and continuous random variables, joint probability distributions, estimation, hypothesis testing, regression, analysis of variance, goodness of fit. (Consult the Credit Exclusion list within the Faculty of Science section of the Calendar). [3-1-0]
Prerequisite: One of MATH 101, MATH 103, MATH 105, MATH 121, SCIE 001.
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 200, STAT 203, STAT 241, STAT 251, BIOL 300, COMM 291, ECON 325, ECON 327, FRST 231, KIN 206, LFS 252, POLI 380, PSYC 218, PSYC 278, PSYC 366.
Equivalency: COMM 411.
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 201 and one of MATH 100, MATH 102, MATH 104, MATH 110, MATH 120, MATH 180, MATH 184, SCIE 001.
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 200, MATH 226, MATH 217, MATH 253, MATH 254.
Equivalency: MATH 302.
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 200, STAT 203, BIOL 300, STAT 241, STAT 251, COMM 291, ECON 325, FRST 231, PSYC 218, PSYC 366 and one of MATH 302, STAT 302; or (b) a score of 65% or higher in one of MATH 302, STAT 302. The Department recommends that students meet the prerequisite through option (a).
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 152, MATH 221, MATH 223 and one of STAT 200, STAT 241, STAT 251, STAT 300, BIOL 300, COMM 291, ECON 325, ECON 327, FRST 231, PSYC 218, PSYC 278, PSYC 366 and one of MATH 302, STAT 302.
Implementing theory in applications. Problem based learning. Generation and analysis of case data. Modelling, computation and reporting. [0-4-0]
Corequisite: STAT 306.
Continuation of STAT 307. [0-2-0]
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: ELEC 321.
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.
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.
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. This course is not eligible for Credit/D/Fail grading.
Prerequisite: Registration in Statistics Honours or Major Program.
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. This course is not eligible for Credit/D/Fail grading.
Prerequisite: STAT 398.
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 305.
Corequisite: STAT 306.
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: One of STAT 306, CPSC 340.
Trend and seasonality, autocorrelation, stationarity, stochastic models, exponential smoothing, Holt-Winters methods, Box-Jenkins approach, frequency domain analysis. [3-0-1]
Prerequisite: One of MATH 302, MATH 318, STAT 302 and one of STAT 200, STAT 241, STAT 251, STAT 300, BIOL 300, COMM 291, ECON 325, ECON 327, FRST 231, POLI 380, PSYC 218, PSYC 278, PSYC 366.
Corequisite: STAT 305.
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: STAT 306.
Students should consult the Statistics Department for the particular topics offered in a given year.
Prerequisite: STAT 305.
A research project, undertaken under the supervision of a faculty member, resulting in a written report.
Prerequisite: Open to fourth year students enrolled in a Statistics Honours specialization. Permission of the Undergraduate Advisor and supervising faculty member is required.
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: STAT 306.
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.
Hypothesis testing and model selection in modern statistics, confidence regions, multiple testing, model comparison criteria. Intended for Honours students. [3-0-0]
Prerequisite: STAT 460.
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.
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.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
Equivalency: BIOF 540, GSAT 540.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
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.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
Pass/Fail This course is not eligible for Credit/D/Fail grading.
Credit will not be given for both STAT 460 and STAT 560. This course is not eligible for Credit/D/Fail grading. [3-0-0]
This course is not eligible for Credit/D/Fail grading.
This course is not eligible for Credit/D/Fail grading.
Restricted to students admitted to the Co-operative M.Sc. Education Program in Statistics. This course is not eligible for Credit/D/Fail grading.
Restricted to students admitted to the Co-operative M.Sc. Education Program in Statistics. This course is not eligible for Credit/D/Fail grading.
Prerequisite: STAT 598.
This course is not eligible for Credit/D/Fail grading.