| Course Number | Course Title | Day & Time | Course Fee | Register |
|---|---|---|---|---|
| PSY 317L | Introduction to Statistics for the Behavioral Sciences | Online | $895.00 |
Understanding statistics and data is a fundamental skill for everybody. Being able to draw conclusions from data and gain understanding and insights into the world is not just the basis of all sciences including psychology, neuroscience, and medicine, but also social sciences and public policy research. Specifically, we will cover fundamental topics such as frequency distributions, central tendency, variability, probability, hypothesis testing, t-tests (for independent and paired samples), effect sizes, statistical power, estimation using confidence intervals, correlation, regression, non-parametric statistics, linear regression and the basics of randomization tests. This course will also highly emphasize learning how to use the statistical programming language R to manage, analyze, visualize and communicate data.
View syllabus.
This course is independent study and is self-paced. Students have five months upon registration in which to complete all coursework and exams.
Textbooks and Materials
All required materials for the course are available for free online:
- Curley JP & Milewski TM, PSY317L Guidebook
- Navarro D, Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners
- Wilke C, Fundamentals of Data Visualization
- Grolemund G & Wickham H, R for Data Science
Technical Requirements
Students are required to use a laptop or desktop computer with a high-speed internet connection. Tablets and smartphones are not supported. The computer should have a modern and updated operating system, at least 2GB of RAM, a webcam, a microphone and also meet Honorlock's system requirements for online testing. Students are required to download and install R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com) as well as a zip folder containing R files and datasets.
Explore the R programming language, data visualization, data wrangling, descriptive statistics (measures of central tendency and variation), measures of association (correlation, regression). Examine inferential statistics including parametric tests (such as t-tests and ANOVA) and non-parametric tests (such as Kruskal-Wallis and chi-squared tests).
May be counted towards the Quantitative Reasoning flag requirement.
None.
Students should contact the University Extension advisor with any questions about prerequisites or placement in our courses. All students should contact their academic advisor with any questions about how this course fulfills their degree requirements.
