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.

Syllabus

Week 1
Foundations of Regression, OLS Modelling & Multicollinearity
- Course Overview
  Regression analysis is an important topic in Statistics and Machine Learning. In this course, you will learn the key concepts in detail from beginner to expert level, using practical examples.
- Get Started with Practical Regression Analysis in R
  In this activity, you will learn about the Introduction of course, understand the difference between statistical analysis and machine learning, setting up R & RStudio, practice basic data cleaning and data exploration.
- Ordinary Least Square Regression Modelling
  In this activity you will master the theory, implementation, and evaluation of Ordinary Least Square (OLS) linear regression models in R.
- Deal with Multicollinearity in OLS Regression Models
  In this activity you will learn how to detect collinearity among predictors and apply modern regularized and dimension-reduction regression techniques.
Week 2
Model Selection, Diagnostics, GLMs, and Non-Linear Machine Learning
- Variable & Model Selection
  In this activity you will explore automated and theory-driven model selection methods to identify optimal predictor subsets and evaluate performance.
- Dealing with Other Violations of the OLS Regression Models
  In this activity you will diagnose and resolve common statistical assumption violations such as non-normality, outliers, and heteroscedasticity.
- Generalized Linear Models (GLMs)
  In this activity you will extend linear regression models to non-Gaussian response variables including binary, multinomial, and count data.
- Working with Non-Parametric and Non-Linear Data
  In this activity you will learn about leverage flexible non-linear, tree-based, and ensemble machine learning algorithms to model complex non-linear structures.

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What will you achieve?

By the end of the course, you‘ll be able to...

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

Who is the course for?

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

Who developed the course?

Packt

Practical Tech Learning, Built for Where the Industry Is Heading

For over 20 years, Packt has helped technologists at every stage turn complex topics into real, usable skills. We’re a global technology learning publisher with a library of more than 9,000 hands-on tutorials, and we’re adding to it constantly.

What's included?

This is a premium course. These courses are designed for professionals from specific industries looking to learn with a smaller group of like-minded individuals.

Unlimited access to this course
Includes any articles, videos, peer reviews and quizzes
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