Events

In this workshop, we aim to provide a practical introduction to statistical methods used for comparing group means, including various types of t-tests, analysis of variance (ANOVA), and post-hoc analysis techniques. Participants will learn when and how to apply independent and paired t-tests, interpret ANOVA results, and choose appropriate post-hoc tests to explore significant findings. Through examples and hands-on exercises, attendees will gain confidence in selecting and executing the suitable statistical tests for their data.

Are you ready to transform messy datasets into clean, structured insights? This workshop introduces you to dplyr -- a powerful R package for data manipulation.

Through practical examples, you’ll discover how to:

• Combine multiple operations seamlessly with pipes

• Select and arrange columns and rows for clarity

• Filter and group data to uncover patterns
• Summarize and transform datasets for analysis

Whether you’re a beginner or looking to sharpen your skills, you’ll have the confidence to manage data like a pro and streamline your analysis by the end of the workshop. 

Data are often stored in tables or databases, which provide information about size and variable types but reveal little about underlying patterns. This online workshop introduces graphical and numerical techniques to uncover data structure and summarize distributions effectively.

Refresh your understanding of fundamental statistical concepts and strengthen your data analysis skills in this interactive workshop. Using R, we’ll revisit key topics such as descriptive statistics, probability distributions, hypothesis testing and basic inferential techniques. Through guided exercises, you’ll learn how to apply these concepts effectively to interpret and analyze data with confidence. 

Linear regression models, also known as linear models, are essential tools in various fields due to their simplicity and ease of interpretation. This workshop offers a comprehensive introduction to both simple and multiple linear regression.

What You'll Learn:

• Fundamentals of regression models and their assumptions
• How to interpret coefficients and conduct significance testing
• Exploring interactions between variables and using dummy variables
• Model-checking techniques, including residual plots and collinearity diagnostics
• Methods for model selection