## Jeremy VanderDoes published in Human Vaccines and Immunotherapeutics

Congratulations to Jeremy VanderDoes for his recent publication in the Human Vaccines & Immunotherapeutics.

Monday, July 18, 2022

Congratulations to Jeremy VanderDoes for his recent publication in the Human Vaccines & Immunotherapeutics.

Monday, May 9, 2022

Kelly Ramsay (BSc (Statistics and Actuarial Science), 2016, University of Manitoba, MSc (Statistics), 2018, University of Manitoba, Ph.D. (Statistics), 2022, University of Waterloo) will be joining the Department of Mathematics and Statistics at York University in July as an Assistant Professor.

Tuesday, July 6, 2021

One of our graduate consultants, Meixi Chen, performed an in-depth data analysis for a project led by Dr. Wasem Alsabbagh (School of Pharmacy). Their collaborative article is accepted and published in *Addiction*. Congratulations Meixi!

Many categorical response variables have a natural order to their levels. We refer to them as ordinal variable (data). This workshop will show participants how to estimate and make inferences about ordinal data through the proportional odds model. Topics covered in this workshop includes introducing the proportional odds model, discussing the model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods such as residual plots and goodness-of-fit tests will also be covered. Several methods for model selection will be included.

Registration is free and open to all University of Waterloo faculty, staff, graduate and undergraduate students. The primary software we will discussed in this seminar is RStudio. There is no hands-on work in this seminar.

Feature selection is the process of selecting a subset of relevant features (commonly known as predictors or independent variables) for model construction. Performing feature selection allows researchers to identify irrelevant data, improve the interpretation and increase predictive accuracy of learned models. A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation which scores the different feature subsets. The choice of evaluation measure heavily influences the algorithm. There are three main categories of feature selection algorithms: wrappers, filters and embedded methods. In this seminar, we will introduce some basic feature selection methods such as score-based feature ranking, stepwise subset selection and LASSO regression.

Registration is free and open to all University of Waterloo faculty, staff, graduate and undergraduate students. The primary software we will discussed in this seminar is RStudio. There is no hands-on work in this seminar.

In this introduction to R workshop, participants will be taught the basics of this open source language. Topics covered in this workshop includes:

- Help tools
- Importing / exporting data
- Data management
- Descriptive and exploratory statistics
- Graphics
- Common statistical analyses

Registration is free and open to all University of Waterloo faculty, staff, graduate and undergraduate students. No programming experience is assumed.

This workshop will show participants how to estimate and make inferences about a binary response probability and related quantities through logistic regression. Topics covered in this workshop includes the logistic regression model, model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods such as residual plots and goodness-of-fit tests will also be covered. Several methods for model selection will be included.

Registration is free and open to all University of Waterloo faculty, staff and graduate students.

This workshop will provide participants with an introduction to Poisson regression used to model counts observed in a period of time. Topics covered in this workshop includes the Poisson regression model, model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods such as residual plots and goodness-of-fit tests will also be covered. Several methods for model selection will be included.

Registration is free and open to all University of Waterloo faculty, staff and graduate students.

This workshop will provide participants with an introduction to simple and multiple linear regression. Topics covered in this workshop includes the regression models, model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods such as residual plots and collinearity diagnostics will also be covered. Several methods for model selection will be included.

Registration is free and open to all University of Waterloo faculty, staff and graduate students.