Statistics Tutorials in R for Modelling and Data Analysis

This section collects statistics tutorials in R for modelling, prediction, and data analysis. Topics include linear regression, geom_smooth(), artificial neural networks, Bayesian statistics, multiple testing, Simpson’s paradox, and applied statistical workflows.

Featured statistics tutorials

• How geom_smooth() makes linear model predictions compares ggplot2, lm(), and predict() using historical cholera data.
• Plain vanilla neural network from scratch in R explains activation functions, weights, and backpropagation without hiding the mechanics.
• R-Ladies event text analysis uses Meetup event data, tidy text methods, and word cloud visualization.
• Bayesian statistics model comparison compares Bayesian model packages, priors, likelihoods, and posterior inference.
• Predictive modelling in R compares caret and tidymodels for preprocessing, resampling, training, and evaluation.