Workshop: R for Statistical Analysis


Workshop: R for Statistical Analysis

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Team: Events, Training & News
Posted on December 18, 2018

CHIMb.ca
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Workshop: R for Statistical Analysis

Team: Events, Training & News

Date: This is not a timed event.

Description


R is a highly adaptable, no-cost programming environment for statistical analysis and visualization. You can learn basic, intermediate, and/or advanced skills with the Centre for Healthcare Innovation Data Science platform’s R workshops. 

Register for any or all for the workshops based on your level of experience with R at the following link: https://www.cpd-umanitoba.com/?post_type=espresso_events&p=25904&preview=true 

BEGINNER: R FOR ABSOLUTE BEGINNERS
February 4, 6, 11 & 13 • 1:30 - 3:30 pm

Rm 474 Chown Building, 753 McDermot Ave, University of Manitoba, Winnipeg, MB

This workshop series will introduce participants to R and RStudio by providing instruction and hands-on exercises. Participants will leave with an understanding of basic principals in the use of R for data analysis, including the basics of importing data, data manipulation, and simple graphics. No programming experience is required.

INTERMEDIATE: USING R FOR BASIC STATS
February 25 & 27 and March 4 & 6 • 1:30 - 3:30 pm

Rm 207 Chown Building, 753 McDermot Ave, University of Manitoba, Winnipeg, MB

This hands-on workshop will get you ready to do basic statistical analyses in R. Previous introductory experience with R (e.g, our “R for Absolute Beginners”) will be assumed. We will discuss: odds ratios, correlations, t-tests, chi-squared tests, and linear and logistic regression.

ADVANCED: MULTI-LEVEL MODELS FOR HIERARCHICAL DATA WITH R 
March 11 & 13 • 1:30 - 4:30 pm

Rm 207 Chown Building, 753 McDermot Ave, University of Manitoba, Winnipeg, MB

Multilevel models are extensions of regression for data that have a hierarchical (or nested) structure.  This two half-day workshop provides an intensive introduction to multilevel models. The workshop will focus on hands-on understanding and draw from examples across the social and health sciences. Previous experience with R and linear regression with any software will be assumed.

 

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