If you are a graduate student involved in research that engages with the community, or if you have a strong interest in improving your community, you may find this tactical urbanism workshop appealing. The workshop aims to provide a comprehensive understanding of tactical urbanism by discussing its concept and presenting examples from international and Canadian contexts.

This series introduces early-career researchers to statistical models that extend beyond linear models (i.e., ANOVAs) so that they may learn how to *fit models to their data rather than fitting their data to models*.

This workshop will introduce linear models (i.e., one-way ANOVAs), their assumptions, and limitations, in a format tailored towards visual and spatial learners.

This session will introduce participants to the foundational concepts of statistical inference, including population distributions and the process of random sampling. Attendees will learn how sampling distributions evolve towards normality as sample sizes increase and will visually explore the Central Limit Theorem.

By the end of the session, participants should be able to visualize and understand population distributions, illustrate random sampling processes, recognize the normalizing effect of larger samples on sampling distributions, and demonstrate the Central Limit Theorem visually.

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.

At the end of this workshop, you will have an understanding of the different steps that lead to the development of clinical practice guidelines, and will be able to perform data extraction, meta-analysis, and to assess the quality of the evidence.

This workshop will illustrate how to fit linear models in R, diagnose any issues with model assumption violations, and interpret linear model summaries, including model coefficients, degrees of freedom, standard error estimates, t statistics, F statistics, p-values, R2, statistical significance, adjusted R2.

By the end of this session, participants will be able to fit linear models in R and interpret model outputs, including the output of the summary() function in R.

Are you a researcher who wants to publish your work in a journal or book? Do you know how to negotiate the best terms and conditions for your publication? Do you understand your rights and responsibilities as an author? If not, this workshop is for you!

This session will address the visualization of standard deviation (s.d.), standard error of the mean (s.e.m.), and confidence interval (CI) error bars to enhance the understanding of uncertainty in data analysis. The interpretation of error bars for statistical significance will be discussed, along with common misinterpretations to avoid.

By the end of the session, participants should be able to visualize and interpret error bars, understand the implications of their spacing and width, and be cautious of common pitfalls such as misinterpreting non-overlapping error bars as evidence of significance.

This workshop will demystify ANOVAs by framing them in the context of linear models with multiple predictors (i.e., multiple linear regression). The session will also introduce attendees to Directed Acyclical Graphs (DAGs) and demonstrate how to use them to infer causality in one’s model.

By the end of this session participants should be able to fit linear models with more than one predictor, check for collinearity between predictors, and interpret linear models using DAGs.

This session will introduce participants to the concept of P values and their role in hypothesis testing, highlighting that P values reflect the probability of observing the data under the null hypothesis, not the biological significance of the findings. The session will cover the computation of P values and delve into the nuances of one-sample t-tests.

By the end of the session, participants should be able to comprehend the meaning of P values, understand how hypothesis tests calculate P values, recognize when small P values indicate unlikely events under the null hypothesis, and explore the assumptions behind one-sample t-tests.

We’ll explore strategies and provide sample abstracts and lay summaries from diverse fields and purposes, including theses, conferences, and funding applications.

Learn all about the publishing process, get some tips for selecting the right journal for you, and develop your skills for responding to peer reviewers at this online workshop.

Tired of solo writing sessions? Craving a supportive environment to fuel your creativity? Look no further than our weekly Writing Community, tailored for the UBCO community.

Are you a graduate student who is working on your thesis or dissertation? 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!