Regression Analysis for Statistics & Machine Learning in R

In this module, we will introduce you to the essential concepts and tools for regression analysis in R. You'll learn the differences between statistical analysis and machine learning, get familiar with R and R Studio, and start working with data. We'll guide you through the steps of data cleaning and perform some initial exploratory data analysis.

In this module, we will delve into Ordinary Least Squares (OLS) regression, covering both theory and practical implementation in R. You will learn how to interpret OLS results, calculate and apply confidence intervals, and explore various OLS regression techniques.

In this module, we will address the challenge of multicollinearity in OLS regression models. You will learn how to detect multicollinearity and manage regression analyses with correlated predictors.

In this module, we will explore the critical aspects of variable and model selection in regression analysis. You will understand why selection is essential, learn how to choose the most appropriate OLS regression model, and identify model subsets.

In this module, we will tackle common violations of OLS regression model assumptions. You will learn how to apply data transformations to correct issues, use robust regression methods to manage outliers, and address heteroscedasticity.

In this module, we will introduce you to Generalized Linear Models (GLMs) and their various applications. You will learn the fundamentals of GLMs, including logistic regression for binary response variables, multinomial logistic regression, and regression techniques for count data.

In this module, we will explore advanced methods for working with non-parametric and non-linear data. You will learn to implement polynomial and non-linear regression techniques, use Generalized Additive Models (GAMs) and their boosted versions, and develop Multivariate Adaptive Regression Splines (MARS) models.