This course, offered by Illinois Tech, caters to individuals with a technical background in mathematics, statistics, computer science, or engineering seeking a transition to data-driven industries such as finance, retail, tech, healthcare, and government.
Participants will delve into the deeper aspects of simple and multiple linear regression, learning to identify and rectify discrepancies in model assumptions. They will gain proficiency in using diagnostic plots to detect violations, performing variable selection and model validations, and employing suitable tools to address heteroscedastic errors, autocorrelation, and collinear data.
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The course comprises three modules providing a deep dive into model diagnostics and remediation. Participants will learn about regression diagnostics, variance-stabilizing transformation, weighted least squares, autocorrelation, multicollinearity, and model validation.
Module 1 introduces participants to regression diagnostics, laying the foundation for understanding model assumptions and diagnostic techniques. They will delve into variance-stabilizing transformations and Box-Cox transformations, gaining insights into transforming variables to linearize the model.
Module 2 delves deeper into model diagnostics, covering weighted least squares, autocorrelation, multicollinearity, and video selection for model validation. Participants will gain a comprehensive understanding of these vital aspects of model remediation.
The summative assessment module allows participants to apply their knowledge and skills acquired throughout the course, ensuring a thorough understanding of model diagnostics and remedial measures.
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