Machine Learning Workshop Part 1 | Linear Regression with JASP

Welcome to Part 1 of our comprehensive two-day Machine Learning Workshop, focusing on Linear Regression using the user-friendly software, JASP! This introductory session is designed to equip you with essential knowledge and tools for predictive modeling in language assessment.

In this video, Dr. Vahid Aryadoust leads us through the fundamentals of the general linear model, specifically the linear regression model. Learn about key statistical concepts such as linearity, independence, homoscedasticity, normality, and the absence of multicollinearity. Dr. Aryadoust explains how to check for these assumptions using JASP, ensuring you have a solid foundation in statistical inference.

Topics Covered:

Linearity: Understanding the linear relationship between dependent and independent variables.
Independence: Ensuring observations are independent (using the Durbin-Watson Test).
Homoscedasticity: Maintaining constant variance of error terms across all levels of independent variables.
Normality: Checking for normally distributed residuals.
No Multicollinearity: Ensuring independent variables are not highly correlated.
This workshop is supported by a grant from the UK Association for Language Testing and Assessment (UKALTA). Each session is approximately two hours long and offers practical insights into using JASP for statistical analysis and machine learning.

Interested participants are encouraged to register by scanning the barcode provided to receive further information.

Stay tuned for Part 2, where we will delve deeper into machine learning techniques and hands-on experiences with GUI software!

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