All other courses in Introductory Statistics (except

Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.

One or more of Ontario Secondary School MCV4U, MHF4U, MDM4U,

the former Psychology 2800E, the former Psychology 2820E,

This course will introduce students to the variety of ways to conduct research in psychology. Topics to be covered include the scientific approach to testing psychological theory; ethical issues in psychological research; developing and planning research projects; basics of measurement; survey research; best practices in research conduct.

At least 60% in 1.0 credits of Psychology at the 1000 level;

and 0.5 credit of Year 1 Math from among the following courses:

or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Math 1228A/B is recommended. Students who have completed Statistics 1024A/B (or other introductory statistics course) instead of

must seek special permission of the instructor to enrol.

This course expands on basic statistics with an aim at developing an understanding of the use and interpretation of statistics in more complex Psychological research designs. Topics include repeated samples testing, estimation, analysis of variance, correlation, simple linear regression and non-parametric tests.

the former Psychology 2810, the former Psychology 2820E,

An introduction to the nature of geographical analysis of data and the application of statistical techniques and computing systems to in Geography: data collection, research design, sampling; models of spatial data, probability, distributions, hypothesis testing, correlations and regression.

; or enrolment in the Major in Physical Geography and Environment, in the Certificate in Geographic Information Science, or in an Honours Earth Science Program for Professional Registration.

All other courses or half courses in Introductory Statistics, except

Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.

Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics.

or the former Applied Mathematics 1412A/B), (

or the former Applied Mathematics 1414A/B),

or the former Applied Mathematics 1413, the former

An introductory course in the application of statistical methods, intended for honours students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation. Cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling other than the Minor in Applied Statistics.

1.0 Mathematics course or equivalent numbered 1000 or above.

can be used to meet 0.5 of the 1.0 mathematics course requirement.

An introduction to econometric description and inference which covers: descriptive statistics for cross-section and time-series samples; probability, discrete and continuous probability distributions, random variables; estimators and sampling distributions; confidence intervals and tests of hypotheses; simple linear regression for normal and non-normal populations.

An introduction to measurement and analysis in health sciences research, covering topics such as validity, reliability, standard errors, confidence intervals, tests of means, correlation, and linear regression.

An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests.

An introduction to statistics with emphasis on the applied probability models used in Electrical and Civil Engineering and elsewhere.  Topics covered include samples, probability, probability distributions, estimation (including comparison of means), correlation and regression. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics or the Minor in Applied Financial Modeling.

This course introduces students to the basics of data analysis for psychological research. Topics include probability, sampling, estimation, data visualization, and the conduct and interpretation of basic statistical analyses. Throughout the term, students will gain experience in computer-based data analytic methods.

or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator.

is recommended. In addition to completion of 1.0

-level course, students who have completed

(or other introductory statistics course, in addition to 0.5 credit of Year 1 Math) may enrol after completing an introductory programming class from the following list:

for students entering the program with 1.0 Year 1 Math courses.

This course provides an introduction to techniques of statistical analysis with an emphasis an application in Psychology. Topics include descriptive statistics, the normal distribution, probability, inferential statistics, and hypothesis testing. Also included will be discussion on the interpretation of statistics in other research designs including correlation, repeated samples testing and analysis of variance.

A mark of at least 60% in 1.0 credits of Psychology at the 1000 level.

An introduction to econometric description and inference which covers: descriptive statistics for cross-section and time-series samples, probability; probability distributions and random variables; estimators and sampling distributions; confidence intervals and tests of hypotheses; simple linear regression.

The purpose of this course is to introduce students to quantitative decision-making skills, with an emphasis on analysis techniques used in management. Topics include: descriptive statistics, probability, hypothesis testing, analysis of variance, correlation and regression, time series forecasting, and survey techniques.

An advanced treatment of the collection, analysis and interpretation of behavioral data, reviewing and extending the topics covered in Psychology 1100E; problems in the design and conduct of studies and experiments, operational definitions, experimental control and recording of behavior; statistical treatment of data, including descriptive and inferential statistics.

An introduction to the theory of statistics with strong links to data as well as its probabilistic underpinnings. Topics covered include estimation and hypothesis testing, sampling distributions, linear regression, experimental design, law of large numbers and central limit theorem.

An introduction to the techniques of statistical analysis used by sociologists, including descriptive statistics, the normal curve, hypothesis testing and various measures of association.

the former Psychology 2810, the former Psychology 2820E, the former Social Work 2207A/B,

An introductory course in the application of statistical methods, intended for students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation.

A data-driven introduction to statistics intended primarily for students in Chemical and Mechanical Engineering. Exploratory data analysis, probability, the Binomial, Poisson, Normal, Chi-Square and F distributions. Estimation, correlation and regression (model building and parameter estimation), analysis of variance, design of experiments. This course cannot be taken for credit in any module in Data Science, Statistics, Actuarial Science, or Financial Modelling, other than the Minor in Applied Statistics or the Minor in Applied Financial Modeling.

STATS 2143A/B

APPLIED STATISTICS AND DATA ANLYSIS FOR ENGINEERS

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