Statistical Inference with R
A practical introduction to data analysis that covers visualization, wrangling, and linear modeling using R and the tidyverse. It connects these data science tools directly to statistical inference, exploring confidence intervals and hypothesis testing through real-world case studies.
What This Course Covers
Statistical Inference with R is structured into 11 chapters that build on each other progressively:
Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.
How to Study Statistical Inference with R on Lambdio
Lambdio's AI-powered platform adapts to how Computer Science courses are best learned. Here's our recommended approach:
Statistical Inference with R is a code-heavy, applied subject that blends R programming with statistical theory. Standard Mode is the right choice because learning R syntax, ggplot2 layers, and dplyr verbs requires structured explanations, code examples, and hands-on practice — all of which Standard Mode delivers through step-by-step tutorials with comprehension checks. Socratic Mode is less suitable since discovering R syntax or bootstrapping protocols through questions alone would be inefficient without direct code demonstrations. The Medium difficulty of this course, combined with its mix of conceptual statistics and applied coding, makes Medium priority the ideal default — the spaced repetition algorithm will schedule balanced reviews so that both R syntax and statistical reasoning remain fresh. For quick check-ins on terminology and key concepts, use Quiz Mode to reinforce knowledge before moving to more advanced data analysis projects.
Interactive Quiz
Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:
%>% do in R?What You'll Be Able to Do After This Course
- ✓Write and execute R code using objects, vectors, factors, data frames, and logical operators
- ✓Create exploratory and publication-quality visualizations using ggplot2 and the Grammar of Graphics
- ✓Manipulate and transform datasets using dplyr verbs and the pipe operator for chaining operations
- ✓Import external data from CSV and Excel files and reshape datasets into tidy format
- ✓Apply bootstrapping to construct confidence intervals for population parameters
- ✓Conduct hypothesis tests using randomization-based and theoretical frameworks
- ✓Fit and interpret simple and multiple linear regression models for explanation and prediction
- ✓Evaluate regression model assumptions using the LINE framework and draw statistically valid inferences
Frequently Asked Questions
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