Statistical Inference and Intro ML

This unit develops core statistical inference skills and introduces foundational machine learning workflows in R. Students will deepen understanding of sampling distributions, the Central Limit Theorem, hypothesis testing, and confidence intervals while learning to build, visualize, and evaluate basic models and interactive dashboards using R tools and packages.

Learning Objectives

• Analyze sampling distributions and the Central Limit Theorem by calculating and interpreting means, standard deviations, z-scores, standard scores, and confidence intervals using R
• Apply hypothesis testing and sampling distribution concepts to conduct and interpret one-sample and two-sample tests, p-values, and confidence bounds using R functions and simulations
• Demonstrate building, visualizing, and assessing basic supervised and unsupervised models (decision trees, random forests, k-means, neural networks) using R packages (e.g., rattle, caret) and present results with ggplot2
• Create and evaluate interactive dashboards and reproducible workflows in RStudio and Shiny that communicate model performance, separability issues, and limitations to support data-driven decisions