You will not receive additional credit towards your degree for any course that overlaps in content with a course for which you have already received credit at McGill, at another university, or at CEGEP; for advanced placement exams; or for advanced level results, International Baccalaureate Diploma, or French Baccalaureate. It is your responsibility to consult with a degree advisor in the Science student advising (SOUSA), or the department offering the course as to whether or not credit can be obtained and to be aware of exclusion clauses specified in the course description in this publication. For detailed information on transfer credits, please refer to McGill's Advanced Standing and Transfer Credit website.
Sometimes, the same course is offered by two different departments. Such courses are called 'double-prefix' courses. When such courses are offered simultaneously, you should take the course offered by the department in which you are obtaining your degree. For example, in the case of double-prefix courses CHEM XYZ and PHYS XYZ, Chemistry students take CHEM XYZ and the Physics students take PHYS XYZ. If a double-prefix course is offered by different departments in alternate years, you may take whichever course best fits your schedule.
Note for Arts students: Credit for computer courses offered by the School of Computer Science is governed by rules specified in each individual course description.
Note for Science, and Bachelor of Arts and Science students: Credit for statistics courses offered by faculties other than Arts and Science requires the permission of the Associate Dean, Student Affairs (Science), except for students in the B.Sc. Major in Environment, who may take required statistics courses in the Faculty of Agricultural and Environmental Sciences necessary to satisfy their program requirements. Credit for computer courses offered by faculties other than Science requires the permission of the Associate Dean, Student Affairs (Science), and will be granted only under exceptional circumstances.
Credit for statistics courses for Arts, Science, and Bachelor of Arts and Science students will be given with the following stipulations:
Credit will be given for only one of the following introductory statistics courses:
Course List
Course
Title
Credits
AEMA 310
Statistical Methods 1.
3
Statistical Methods 1.
Terms offered: Fall 2025, Winter 2026
Measures of central tendency and dispersion; binomial and Poisson distributions; normal, chi-square, Student's t and Fisher-Snedecor F distributions; estimation and hypothesis testing; simple linear regression and correlation; analysis of variance for simple experimental designs.
Terms offered: this course is not currently offered.
Elementary statistical methods in biology. Introduction to the analysis of biological data with emphasis on the assumptions behind statistical tests and models. Use of statistical techniques typically available on computer packages.
Terms offered: this course is not currently offered.
Stochastic phenomena; probability and frequency distributions, introduction to probability theory. Statistical inference about proportions, means and variances; analysis of variance; nonparametric statistics; index numbers and time series; economic forecasting; regression and correlation analysis; introduction to general linear models, its uses and limitations; uses and misuses of statistics.
Terms offered: this course is not currently offered.
This online course provides an introduction to the fundamentals of descriptive and inferential statistics. Topics in descriptive statistics include introduction to statistics, measures of central tendency, variability and correlation. Topics in inferential statistics concentrate on basic procedures in between-group hypothesis testing
using dependent and independent t-tests and within-group hypothesis testing using correlation.
Terms offered: this course is not currently offered.
Exploratory data analysis, univariate descriptive and inferential statistics, non-parametric statistics, correlation and simple regression. Problems associated with analysing spatial data such as the 'modifiable areal unit problem' and spatial autocorrelation. Statistics measuring spatial pattern in point, line and polygon data.
Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms offered: this course is not currently offered.
Statistical concepts and methodology, their application to managerial decision-making, real-life data, problem-solving and spreadsheet modeling. Topics include: descriptive statistics; normal distributions, sampling distributions and estimation, hypothesis testing for one and two populations, goodness of fit, analysis of variance, simple and multiple regression.
Terms offered: this course is not currently offered.
Descriptive statistics, probability, random variables, binomial, poisson, normal distributions, sampling distribution of the mean, estimation, hypothesis testing, analysis of variance, tests of goodness of fit, simple linear regression, non-parametric statistics. Use of computer statistics package (no computer background needed). Application to problems in business and management.
Terms offered: this course is not currently offered.
This is an introductory course in descriptive and inferential statistics. The course is designed to help students develop a critical attitude toward statistical argument. It serves as a background for further statistics courses, helping to provide the intuition which can sometimes be lost amid the formulas.
Students who have already received credit for PSYC 204 Introduction to Psychological Statistics. will not receive credit for any of the following:
Course List
Course
Title
Credits
AEMA 310
Statistical Methods 1.
3
Statistical Methods 1.
Terms offered: Fall 2025, Winter 2026
Measures of central tendency and dispersion; binomial and Poisson distributions; normal, chi-square, Student's t and Fisher-Snedecor F distributions; estimation and hypothesis testing; simple linear regression and correlation; analysis of variance for simple experimental designs.
Terms offered: this course is not currently offered.
Elementary statistical methods in biology. Introduction to the analysis of biological data with emphasis on the assumptions behind statistical tests and models. Use of statistical techniques typically available on computer packages.
Terms offered: this course is not currently offered.
Stochastic phenomena; probability and frequency distributions, introduction to probability theory. Statistical inference about proportions, means and variances; analysis of variance; nonparametric statistics; index numbers and time series; economic forecasting; regression and correlation analysis; introduction to general linear models, its uses and limitations; uses and misuses of statistics.
Terms offered: this course is not currently offered.
This online course provides an introduction to the fundamentals of descriptive and inferential statistics. Topics in descriptive statistics include introduction to statistics, measures of central tendency, variability and correlation. Topics in inferential statistics concentrate on basic procedures in between-group hypothesis testing
using dependent and independent t-tests and within-group hypothesis testing using correlation.
Terms offered: this course is not currently offered.
Exploratory data analysis, univariate descriptive and inferential statistics, non-parametric statistics, correlation and simple regression. Problems associated with analysing spatial data such as the 'modifiable areal unit problem' and spatial autocorrelation. Statistics measuring spatial pattern in point, line and polygon data.
Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms offered: this course is not currently offered.
Statistical concepts and methodology, their application to managerial decision-making, real-life data, problem-solving and spreadsheet modeling. Topics include: descriptive statistics; normal distributions, sampling distributions and estimation, hypothesis testing for one and two populations, goodness of fit, analysis of variance, simple and multiple regression.
Terms offered: this course is not currently offered.
Descriptive statistics, probability, random variables, binomial, poisson, normal distributions, sampling distribution of the mean, estimation, hypothesis testing, analysis of variance, tests of goodness of fit, simple linear regression, non-parametric statistics. Use of computer statistics package (no computer background needed). Application to problems in business and management.
Terms offered: this course is not currently offered.
This is an introductory course in descriptive and inferential statistics. The course is designed to help students develop a critical attitude toward statistical argument. It serves as a background for further statistics courses, helping to provide the intuition which can sometimes be lost amid the formulas.
Credit will be given for only one of the following intermediate statistics courses:
Course List
Course
Title
Credits
AEMA 411
Experimental Designs 01.
3
Experimental Designs 01.
Terms offered: Winter 2026
General principles of experimental design, split-plot designs, spatial heterogeneity and experimental design, incomplete block designs and unbalanced designs, analysis of repeated measures, multivariate and modified univariate analyses of variance, central composite designs.
Terms offered: this course is not currently offered.
Stochastic phenomena; probability and frequency distributions, introduction to probability theory. Statistical inference about proportions, means and variances; analysis of variance; nonparametric statistics; index numbers and time series; economic forecasting; regression and correlation analysis; introduction to general linear models, its uses and limitations; uses and misuses of statistics.
Terms offered: this course is not currently offered.
The concept of degrees of freedom and the analysis of variability. Planning of experiments. Experimental designs. Polynomial and multiple regressions. Statistical computer packages (no previous computing experience is needed). General statistical procedures requiring few assumptions about the probability model.
An introduction to the design and analysis of experiments, including analysis of variance, planned and post hoc tests and a comparison of anova to correlational analysis.
Terms offered: this course is not currently offered.
This course blends theory and applications in regression analysis. It focuses on fitting a straight line regression using matrix algebra, extending models for multivariate analysis and discusses problems in the use of regression analysis, providing criteria for model building and selection, and using statistical software to apply statistics efficiently.
with the exception that you may receive credit for both PSYC 305 Statistics for Experimental Design. and ECON 227D1 Economic Statistics./ECON 227D2 Economic Statistics. or ECON 257D1 Economic Statistics - Honours./ECON 257D2 Economic Statistics - Honours.
Students who have already received credit for MATH 324 Statistics. or MATH 357 Honours Statistics. will not receive credit for any of the following:
Course List
Course
Title
Credits
AEMA 310
Statistical Methods 1.
3
Statistical Methods 1.
Terms offered: Fall 2025, Winter 2026
Measures of central tendency and dispersion; binomial and Poisson distributions; normal, chi-square, Student's t and Fisher-Snedecor F distributions; estimation and hypothesis testing; simple linear regression and correlation; analysis of variance for simple experimental designs.
General principles of experimental design, split-plot designs, spatial heterogeneity and experimental design, incomplete block designs and unbalanced designs, analysis of repeated measures, multivariate and modified univariate analyses of variance, central composite designs.
Terms offered: this course is not currently offered.
Elementary statistical methods in biology. Introduction to the analysis of biological data with emphasis on the assumptions behind statistical tests and models. Use of statistical techniques typically available on computer packages.
Terms offered: this course is not currently offered.
Stochastic phenomena; probability and frequency distributions, introduction to probability theory. Statistical inference about proportions, means and variances; analysis of variance; nonparametric statistics; index numbers and time series; economic forecasting; regression and correlation analysis; introduction to general linear models, its uses and limitations; uses and misuses of statistics.
Terms offered: this course is not currently offered.
Exploratory data analysis, univariate descriptive and inferential statistics, non-parametric statistics, correlation and simple regression. Problems associated with analysing spatial data such as the 'modifiable areal unit problem' and spatial autocorrelation. Statistics measuring spatial pattern in point, line and polygon data.
Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms offered: this course is not currently offered.
The concept of degrees of freedom and the analysis of variability. Planning of experiments. Experimental designs. Polynomial and multiple regressions. Statistical computer packages (no previous computing experience is needed). General statistical procedures requiring few assumptions about the probability model.
Terms offered: this course is not currently offered.
Statistical concepts and methodology, their application to managerial decision-making, real-life data, problem-solving and spreadsheet modeling. Topics include: descriptive statistics; normal distributions, sampling distributions and estimation, hypothesis testing for one and two populations, goodness of fit, analysis of variance, simple and multiple regression.
Terms offered: this course is not currently offered.
Descriptive statistics, probability, random variables, binomial, poisson, normal distributions, sampling distribution of the mean, estimation, hypothesis testing, analysis of variance, tests of goodness of fit, simple linear regression, non-parametric statistics. Use of computer statistics package (no computer background needed). Application to problems in business and management.
Terms offered: this course is not currently offered.
The statistical analysis of research data; frequency distributions; graphic representation; measures of central tendency and variability; elementary sampling theory and tests of significance.
An introduction to the design and analysis of experiments, including analysis of variance, planned and post hoc tests and a comparison of anova to correlational analysis.
Terms offered: this course is not currently offered.
This is an introductory course in descriptive and inferential statistics. The course is designed to help students develop a critical attitude toward statistical argument. It serves as a background for further statistics courses, helping to provide the intuition which can sometimes be lost amid the formulas.
For 500-level statistics courses not listed above, students must consult a program/department advisor to ensure that no significant overlap exists. Where such overlap exists with a course for which the student has already received credit, credit for the 500-level course will not be allowed.
