Statistical Fundamentals: A Visual Approach Workshop Series—Correlation, Causation and Association

This session will address the concepts of correlation, causation, and association in data. Participants will learn to differentiate between these concepts and to recognize and interpret various types of correlations.

By the end of the session, participants should be able to distinguish between correlation and causation, recognize the impact of confounding variables on associations, evaluate correlation reliability, and understand the significance of correlation results.

This workshop qualifies for the Scholarly Research, Writing, and Publishing Credential offered through the College of Graduate Studies.

Workshop Leader: Nijiati Abulizi, Centre for Scholarly Communication Data Consultant

Nijiati specializes in data wrangling and visualization and statistical analysis. He provides support with R (both base R and tidyverse), RStudio, R Markdown (including bookdown), Python and Jupyter Notebook, Git and GitHub, and QIIME.

Statistical Fundamentals: A Visual Approach Workshop Series

This series will use R and Python to help develop an intuition for fundamental statistical concepts using data visualization. These workshops are equally suitable to those hoping to enhance their ability to interpret common statistical tests and concepts as it is for those applying statistical modelling to their work. No background in statistics is required, but some familiarity with R or Python will be advantageous.

You may wish to review the asynchronous content of either R Fundamentals for Data Analysis or Python Basics for Data Analysis, or keep an eye out for the next time these workshops are offered.

You can either register for the whole series, or register for individual workshops that are most applicable to you.

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