Stats 535: Data Analysis and Modelling, Summer 2019

Professor T. Fiore

Lecture 1: Course Overview, RStudio, R Markdown, Data Types, and Vectors

Lecture 2: Matrices, Lists, and Data Frames

Lecture 3: Factors, Control Statements, Functions, and Reminders about Statistical Inference and Histograms

Lecture 4: More on Functions, Scoping Rules, Reading and Writing Files, and Probability Review

Lecture 5: ggplot2 and dplyr (Download the three Jupyter notebooks 05a, 05b, and 05c from Canvas! This entire lecture was on the three Jupyter notebooks.)

Lecture 6: More Regression with Linear Models, Math and Probability in R, Metropolis-Hastings Algorithm, k-Nearest Neighbors and Classification of Handwritten Digits