Fundamentals of Statistics V: Intro to Mixed Effects Models
Date & Time
Registration Closed / Past Event
This is the last workshop in a 5-part series focused on the fundamentals of statistics.
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:
This webinar series was made possible via joint collaboration between Applied Statistics and Data Science Group and Graduate Pathways for Success Program, funded by the Graduate Pathways for Success Program.
Statistics is a scientific discipline that enables reaching meaningful conclusions from data. To produce reliable results, you need to justify the choice of the applied statistical methods and models as well as validate the underlying assumptions.
This webinar series provides an overview of foundational statistical concepts using examples of various data structures. We will discuss types of study designs, methods, models and appropriate application of statistical tests along with interpretations of the results obtained from different statistical tools. The aim is to equip the attendees with a deeper understanding of the key concepts of statistical methodology, rather than solving specific project problems or providing hands-on guidance.
Each webinar is a self-contained introduction to different statistical concepts, but as topics become increasingly complex with each consecutive webinar, some aspects will be built on concepts taught in the previous sessions. Hence, there is benefit in attending all the webinars.
If you are a graduate student and have questions about your specific project, please see the SOS Program to book a one-hour free statistical consultation.
The Applied Statistics and Data Science Group (ASDa) in the UBC Department of Statistics provides statistical consulting services and participates in collaborative research. ASDa expertise includes problem formulation, translation of research questions into testable statistical hypotheses, design of experiments and sampling plans for surveys, the choice and explanation of statistical methodology, statistical computing and graphics, the interpretation of findings and more. ASDa also plays an active role in continuing education on and off the UBC campus, giving seminars, webinars, hands-on workshops and courses on statistical concepts and methodologies to various departments, research groups and at teaching hospitals.
Nikolas Krstic is a PhD graduate student at the Department of Statistics and a part-time Statistical Consultant with ASDa. While pursuing his previous degrees, he worked as a statistical analyst at the British Columbia Centre for Disease Control (BCCDC), authoring several published papers on a wide range of environmental health topics. Over the past couple of years, he has worked with numerous clients on projects from a variety of different disciplines. During his studies, research and consulting work, he has developed a strong background in regression analysis, spatial statistics and statistical learning.
General registration opens on Monday, January 23th at 9:00 AM.
Priority will be given to UBC graduate students registered in the current academic session. After registering, you will receive confirmation and additional event details within 2 - 3 business days at the e-mail associated with your community.grad.ubc.ca account. If you experience any difficulty using the online registration tool, please e-mail us at firstname.lastname@example.org.
Please email us if you are registered and no longer able to attend this event.