This session will walk students through the doctoral examination process, and strategies for defending in a virtual environment. It includes:
- Steps in the doctoral examination
- Timelines and pressure points
- Defending virtually (what has changed and what is the same)
There will be time for Q & A.
Facilitator:Robyn Starkey's work is focused on assisting UBC doctoral candidates with preparing for their final examinations.
Exploratory Data Analysis (EDA) is essential for understanding your data and a necessary step prior to any testing or modeling. You will learn insightful graphical and numerical techniques for investigating important aspects of your data such as relationships between variables and unusual observations. More specifically you will learn about:
This webinar focuses on the first two crucial steps in a statistical investigation: 1) identify a question and 2) collect the right data to answer this question. You will learn about:
- observational studies and experiments
- scope of inference
- various sampling strategies and their benefits/drawbacks
- key parts of experimental design (randomization, replication, blocking)
- power and sample size calculations
Check out the video below for a teaser from a previous session:
In this webinar, you will learn how to gain insight from a random and unbiased data sample of a population:
- population distribution vs sample mean distribution
- hypothesis testing
- statistical significance
- t-test for comparing two groups and its assumptions
- non-parametric alternatives to t-test and when to use them
- one-way and two-way ANOVA for comparing more than two groups
Check out the video below for a teaser from a previous session:
Understanding relationships is a key part of the scientific inquiry process. You will learn how to describe relationships between two numerical quantities through correlation measures and simple linear regression models. This will also be extended to multiple linear regression for including additional predictor variables. Specific topics include:
- correlation vs causation
- interpretation of regression model coefficients
- assessing the “fit” of a model
- model selection
Check out the video below for a teaser from a previous session:
Learn about generalized linear models (GLM) for categorical and count outcomes. Necessary for understanding and using these models, the following will be covered:
- interpretation of odds, odds ratios and rate ratios
- variable “exposure” times
- overdispersion
- negative binomial regression
- considerations for Likert scale outcomes
- handling excess zeros
Check out the video below for a teaser from a previous session:
This webinar is critical for understanding when linear regression models aren’t applicable and how to model dependent data. Topics that will be covered are:
- consequences of ignoring dependence (increased risk of false conclusions)
- repeated measures and other examples of dependent data
- variance components
- interpretation in the linear and generalized linear mixed effects context
Check out the video below for a teaser from a previous session: