Centre for Scholarly Communication

This workshop will introduce Generalized Linear Models (GLMs), which allow one to model non-Gaussian (i.e., non-normal) data.

By the end of this session, participants will be familiar with the three parts of GLMs (family of distribution, linear predictor, and link function) and will be able to decide what family of distributions and link function to choose for their data. They will also be able to interpret the output of the summary() function and diagnostic plots for (H)GLMs and recognize the limitations of (H)GLMs.

Join us to unlock the secrets to choosing the right scholarly publisher!

Looking for an easy-to-use alternative to NVivo?

This session will introduce participants to the various types of t-tests, including one-sample, two-sample, paired, and one-sided tests. Attendees will learn about the appropriate applications for each type and the visualization techniques that can enhance the interpretation of t-test results.

By the end of the session, participants should be able to apply and visually represent different t-tests, interpret their results, understand the implications of multiple testing corrections, and select the appropriate test for their data.

Looking for data for your graduate thesis or postdoctoral project? This workshop will introduce you to both publicly available and restricted access Statistics Canada data, and how to apply to use restricted access data through UBCO Research Data Centre.

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

This workshop will introduce Generalized Additive Models (GAMs), which allow one to fit models that are complex and nonlinear but easily interpretable, unlike many “black-box” machine learning models.

By the end of this session, participants will be able to fit GAMs in R using the mgcv package and understand the advantages of GAMs over GLMs and LMs.

Take part in this free, hands-on workshop that will introduce participants to the creative and technical basics of photographic storytelling.

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests. Attendees will learn to compare the adaptability of these tests with different data distributions and to visualize their operation.

By the end of the session, participants should be able to choose and apply the appropriate non-parametric tests for their data, visualize the operation of these tests, and understand the challenges of multiple testing with non-parametric methods.

This workshop will show how to interpret GAMs and how to use GAMs to make publication-level figures.

By the end of this session, participants should be able to interpret GAMs and the output of the summary() function, predict from GAMs, and make figures using GAMs.

Unlock the secrets of crafting impactful literature reviews in this interactive online workshop for graduate students. Discover how to synthesize existing research succinctly while maintaining your own critical perspective and voice.

Using accessible platforms like Google Colab, participants will learn to apply AI tools that enhance diagnostic accuracy, personalize treatments, and ultimately improve patient outcomes. This is a must-attend session for healthcare professionals and data scientists eager to address the growing demand for innovative healthcare solutions through AI.

This session will introduce participants to the Analysis of Variance (ANOVA), a statistical method used for comparing the means of three or more groups. The concept of blocking will also be introduced to reduce noise and isolate sources of variation.

By the end of the session, participants should be able to use ANOVA for multi-treatment analysis, implement blocking in experimental design, calculate the F statistic for assessing significance, and appreciate how blocking can improve the efficiency of a study.

This workshop will introduce key ideas using discipline-agnostic examples and common language while illustrating how to view the world from a Bayesian perspective.