the former Psychology 2810, the former Psychology 2820E,

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.

Building on students' knowledge of design and ethical issues in psychological research, this course will introduce more complex designs and data analysis. Students will design and conduct an original research project and communicate the findings in an APA-style written report.

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 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 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.

This course will introduce students to the variety of ways research is conducted in psychology. Topics to be covered include the scientific approach, ethical issues in human and animal research, specific experimental and non-experimental designs, and APA writing style.

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.

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.

Essential information processing and coding skills for students. Includes core concepts of algorithms and data structures; creating programs and scripts to address problems that arise in applied research; examples of data sets and analyses drawn from a variety of disciplines. No previous programming background assumed.

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 Applied Mathematics 1412A/B, the former Applied Mathematics 1413. If

was completed after September 1, 2016 it is an antirequisite, but not if it was completed before that time.

Elementary techniques of integration; applications of Calculus such as area, volume, and differential equations; functions of several variables, Lagrange multipliers. This course is intended primarily for students in the Social Sciences, but may meet minimum requirements for some Science modules. It may not be used as a prerequisite for any Calculus course numbered 1300 or above.

or the former Applied Mathematics 1411A/B.

Matrix algebra including vectors and matrices, linear equations, determinants. This course is intended primarily for students in the Social Sciences, but may meet minimum requirements for some Science modules.

One or more of Ontario Secondary School MCF3M, MCR3U, or equivalent.

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.

Designing, implementing and testing computer programs using Java and MATLAB to fulfill given specifications for small problems using sound engineering principles and processes. Awareness of the engineering aspects of the process of constructing a computer program.

Programming for Data Science is intended for students with little or no background in programming. Design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in a modern programming language.

An introductory survey of the methods and findings of modern scientific psychology. The following topics will be covered: history and methodology, biological psychology, sensation and perception, learning and motivation, verbal and cognitive processes, developmental psychology, social psychology, individual differences (intelligence and personality), and clinical psychology.

Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.

1.0 courses from Mathematics, Calculus, or Applied Mathematics (numbered 1000 and higher) with a minimum mark of 60%.

(with a minimum grade of 60%) can be used to meet 0.5 of the 1.0 mathematics course requirements.

the former Applied Mathematics 1412A/B, the former Applied Mathematics 1413.

Review of limits and derivatives of exponential, logarithmic and rational functions. Trigonometric functions and their inverses. The derivatives of the trig functions and their inverses. L'Hospital's rules. The definite integral. Fundamental theorem of Calculus. Simple substitution. Applications including areas of regions and volumes of solids of revolution.

An enriched version of Calculus 1000A/B. Basic set theory and an introduction to mathematical rigour. The precise definition of limit. Derivatives of exponential, logarithmic, rational trigonometric functions. L'Hospital's rule. The definite integral. Fundamental theorem of Calculus. Integration by substitution. Applications.

Permutations and combinations; probability theory. This course is intended primarily for students in the Social Sciences, but may meet minimum requirements for some Science modules.

Applications of integration, integration using mathematical software packages. Scaling and allometry. Basic probability theory. Fundamentals of linear algebra: vectors, matrices, matrix algebra. Difference and differential equations. Each topic will be illustrated by examples and applications from the biological sciences, such as population growth, predator-prey dynamics, age-structured populations.

or the former Applied Mathematics 1412A/B.

For students requiring the equivalent of a full course in calculus at a less rigorous level than Calculus 1501A/B.  Integration by parts, partial fractions, integral tables, geometric series, harmonic series, Taylor series with applications, arc length of parametric and polar curves, first order linear and separable differential equations with applications.

Properties and applications of vectors; matrix algebra; solving systems of linear equations; determinants; vector spaces; orthogonality; eigenvalues and eigenvectors.

One or more of Ontario Secondary School MCV4U,

The nature of Computer Science as a discipline; the design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in  a modern programming language. Intended for students with little or no background in programming.

Essential information processing skills for humanities students. Includes an introduction to programming; creating programs and scripts to address problems that arise in applied research; examples of data sets and projects drawn from different areas of the humanities and social science. No previous formal programming background required.

The nature of Computer Science as a discipline; the design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in a modern programming language. Intended for students with significant programming experience in at least one high-level block-structured or object-oriented language.

Students who intend to pursue a degree in Actuarial Science, Applied Mathematics, Astronomy, Mathematics, Physics, or Statistics should take this course. Techniques of integration; The Mean Value Theorem and its consequences; series, Taylor series with applications; parametric and polar curves with applications; first order linear and separable differential equations with applications.

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.

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.

PSYCHOL 2811A/B

STATISTICS FOR PSYCHOLOGY I

Difficulty

Useful

Attendance

Written Reviews (0)

Prerequisites

Antirequisites

Extra Info

Locations