Statistical literacy: Linear models as a unifying concept (using R)
Uncovering the foundational concepts that link inferential statistics to deep learning
Linear models are an essential underlying concept for many statistical and machine learning techniques. They provide a framework that unites everything from basic equations like the mean and variance all the way up to complex modern processes like deep learning.
Join expert Rick Scavetta for a crash course in linear models. You’ll explore major themes in data analysis through insightful connections and examples as you develop a deeper understanding of the key concepts that unite what at first glance seem to be disparate techniques.
What you'll learnand how you can apply it
By the end of this live online course, you’ll understand:
 What linear models are
 The mean and twosample ttests as linear models
 Models as bestguess predictions
 The curse of dimensionality
 The minimization of loss functions (residuals)
 Similarities among equations for various situations
 The biasvariance tradeoff
 Complex methods as elaborations of concepts present in simple linear models
And you’ll be able to:
 Understand reported results based on linear models
 Use your newfound knowledge a solid basis for further independent study
This training course is for you because...
 You encounter linear models but aren’t sure what they mean.
 You want to understand how techniques like the mean and variance, ttests, ordinary least squares regression and ANOVA are built on the same fundamental concepts.
 You want to learn how more complex or reiterative methods like clustering, gradient descent, and deep learning are connected to linear models.
 You apply linear models but aren’t sure how to interpret the results.
Prerequisites
 A basic knowledge of R and RStudio
 Familiarity with statistics fundamentals (e.g., simple random samples, systematic versus random error, types of selection bias, and measures for location and spread)
 An RStudio account (You’ll be provided with RStudio Cloud projects preloaded with exercise scripts and datasets.)
Recommended followup:
 Read Practical Statistics for Data Scientists (book)
About your instructor

Rick Scavetta has worked as an independent data science trainer since 2012. Operating as Scavetta Academy, Rick has a close and recurring presence at primary research institutes all over Germany, including many Max Planck Institutes and Excellence Clusters, in fields as varied as primatology, earth sciences, marine biology, molecular genetics, and behavioral psychology.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction (20 minutes)
 Group discussion: What are models and linear models? Where do they appear?
 Lecture: Overview of methods explored in this course
 Q&A
Classic OLS regression (60 minutes)
 Lecture: Defining models; the biasvariance tradeoff; minimizing loss functions
 Demo: The basics of linear models in R
 Handson exercise: Code OLS regression from scratch
 Q&A
Break (5 minutes)
Other statistical tests (30 minutes)
 Lecture: Understanding twosample ttests and ANOVA; the curse of dimensionality
 Group discussion: Similarities to regression
 Demo: Executing ttest and ANOVA as linear models
 Handson exercise: Perform tests in R
 Q&A
Extending linear models (30 minutes)
 Lecture: Elaborating on simple models for regression and ANOVA
 Handson exercises: Explore model forms in R
 Q&A
Break (5 minutes)
Complex methods (20 minutes)
 Lecture: Analytical versus reiterative approaches to minimize the loss function
 Group discussion: Linear models as the basis for advanced methods
 Handson exercise: Execute advanced methods in R
Wrapup and Q&A (10 minutes)