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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.

Join the Centre for Scholarly Communication for an online Q&A session to ask questions and learn more about how to properly format your thesis for submission.

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 workshop will explore the ethics and techniques of mobilizing knowledge through sonic scholarship. By focusing on scholarly podcasts, we will provide an overview of general and specific tips for translating academic research from textual materials into engaging sonic stories.

Join our online workshop, where we'll unveil strategies for crafting the methods and results sections of your thesis.

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.

Join our online workshop for a comprehensive overview of research proposal writing, applicable across various disciplines.

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.

Ever found it challenging to answer the critical “So what” question in your research? Struggle no more!

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 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 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.