Regression Analysis for Statistics & Machine Learning in R (Premium)

Discover OLS linear regression foundations and address multicollinearity in R

Laying a solid foundation in data science requires mastering linear modeling principles within RStudio. Exploring the mechanics and execution of Ordinary Least Squares (OLS) regression enables precise inference from complex datasets, while advanced diagnostics reveal how to detect and resolve issues like multicollinearity.

Explore model selection, GLMs, and non-parametric machine learning

Expanding into robust statistical modeling involves evaluating model selection strategies, conducting cross-validation, and resolving key OLS assumption violations. Navigating Generalized Linear Models (GLMs) including logistic regression alongside non-parametric machine learning algorithms equips practitioners to model non-linear relationships and generate high-accuracy forecasts.

This course is ideal for students interested in getting started with data science applications in the RStudio environment.

This course is ideal for students interested in getting started with data science applications in the RStudio environment.

• Implement and infer Ordinary Least Square (OLS) regression using R
• Apply statistical and ML based regression models to deal with problems such as multicollinearity
• Carry out the variable selection and assess model accuracy using techniques such as cross-validation
• Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier