Exploring term Fall 2023 Change
- ACAN: Arts of Canada
- ADMN: Public Administration
- AE: Art Education
- AGEI: Ageing
- AHVS: Art History and Visual Studies
- ANTH: Anthropology
- ART: Visual Arts
- ARTS: Arts
- ASL: American Sign Language
- ASTR: Astronomy
- ATWP: Academic and Technical Writing Program
- BCMB: Biochemistry and Microbiology
- BIOC: Biochemistry
- BIOL: Biology
- BME: Biomedical Engineering
- CE: Community Engagement
- CHEM: Chemistry
- CIVE: Civil Engineering
- COM: Commerce
- CS: Canadian Studies
- CSC: Computer Science
- CW: Creative Writing (En'owkin Centre)
- CYC: Child and Youth Care
- DHUM: Digital Humanities
- DSST: Disability Studies (DSS)
- ECE: Electrical and Computer Engineering
- ECON: Economics
- ED-D: Educational Psychology and Leadership Studies
- ED-P: Curriculum and Instruction Studies
- EDCI: Curriculum and Instruction Studies
- EDUC: Education
- ENGR: Engineering
- ENSH: English
- ENT: Entrepreneurship
- EOS: Earth and Ocean Sciences
- EPHE: Exercise Science, Physical and Health Education
- ER: Environmental Restoration
- ES: Environmental Studies
- EUS: European Studies
- FA: Fine Arts
- FRAN: French and Francophone Studies
- GDS: Global Development Studies
- GEOG: Geography
- GMST: Germanic Studies
- GNDR: Gender Studies
- GREE: Greek
- GRS: Greek and Roman Studies
- HDCC: Human Dimensions of Climate Change
- HINF: Health Information Science
- HLTH: Health
- HS: Health and Society
- HSD: Human and Social Development
- HSTR: History
- HUMA: Humanities
- IB: International Business
- ICDG: Indigenous Community Development and Governance
- IED: Indigenous Education
- IGOV: Indigenous Governance
- INGH: Indigenous Health Studies
- INTS: International Health Studies
- IS: Indigenous Studies
- ISP: Intercultural Studies and Practice
- ITAL: Italian
- LAS: Latin American Studies
- LATI: Latin
- LAW: Law
- LING: Linguistics
- MATH: Mathematics
- MDIA: Media Studies
- MECH: Mechanical Engineering
- MEDI: Medieval Studies
- MEDS: Medical Science
- MICR: Microbiology
- MRNE: Marine Science
- MUS: Music
- NURS: Nursing
- PAAS: Pacific and Asian Studies
- PHIL: Philosophy
- PHYS: Physics
- POLI: Political Science
- PORT: Portuguese
- PSYC: Psychology
- RCS: Religion, Culture and Society
- SCIE: Science
- SENG: Software Engineering
- SJS: Social Justice Studies
- SLST: Slavic Studies
- SMGT: Service Management
- SOCI: Sociology
- SOCW: Social Work
- SOSC: Social Science
- SPAN: Spanish
- STAT: Statistics
- TCA: Transformative Climate Action
- THEA: Theatre
- TS: Technology and Society
- VIRS: Visiting International Research Studies
- VKUR: Valerie Kuehne Undergraduate Research Award
- WRIT: Writing
- STAT123: Data Science
- STAT252: Statistics for Business
- STAT254: Probability and Statistics for Engineers
- STAT255: Statistics for Life Sciences I
- STAT256: Statistics for Life Sciences II
- STAT260: Introduction to Probability and Statistics I
- STAT261: Introduction to Probability and Statistics II
- STAT321: Data Management and Presentation
- STAT350: Mathematical Statistics I
- STAT353: Applied Regression Analysis
- STAT354: Sampling Techniques
- STAT355: Statistical Methods in Health Sciences
- STAT359: Data Analysis
- STAT450: Mathematical Statistics II
- STAT453: The Design and Analysis of Experiments
- STAT454: Topics in Applied Statistics
- STAT455: Distribution-Free Statistics
- STAT456: Multivariate Analysis
- STAT457: Time Series Analysis
- STAT458: Generalized Linear Models
- STAT459: Survival Analysis
- STAT460: Bayesian Statistics
- STAT464: Statistical Computing
- STAT465: Statistical Methods for Genomic Data
- STAT466: Robust Statistics
- STAT469: Machine Learning
- STAT498: Seminar and Independent Project
STAT252
Statistics for Business
Descriptive statistics, probability distributions, random sampling and sampling distributions, point and interval estimation, hypothesis testing, statistical inference for comparing samples from two populations, analysis of variance, correlation analysis, simple and multiple linear regression, chi-square test, goodness of fit test, contingency table test, time series analysis. Examples are taken from business applications. Students are expected to analyze data using computer software.
Lecture: 3h
Lab: 0h
Tutorial: 1h
Credits: 1.5