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.

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. Emphasis will be placed on health-related applications.

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.

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

All other courses in Introductory Statistics (except

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 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.

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.

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.

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 introduction to the design of health sciences research, providing students with knowledge relevant to the planning and evaluation of research in both laboratory and applied settings.

HEALTSCI 3801A/B

MEASUREMENT AND ANALYSIS IN HEALTH SCIENCES

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