2017 Spring

Many materials are courtesy of Professor Fritz Scholz.

Lecture notes

R Lecture 01: Introduction to R

R code: R Lecture 01

R Lecture 02: Data Objects (Vectors)

R code: R Lecture 02

R Lecture 03: Other Data Objects

R code: R Lecture 03

R Lecture 04: Functions and Programming

R code: R Lecture 04

R Lecture 05: Data Import and Export

R code: R Lecture 05, ReactionTime.txt, ReactionTime.csv

R Lecture 06: Graphics

R code: R Lecture 06

R Lecture 07: Linear Regression

R code: R Lecture 07

R Lecture 08: Two-Sample Test

R code: R Lecture 08

R Lecture 09: Density Estimation

R code: R Lecture 09, Shiny App: Kernel Density Estimator

R Lecture 10: Classification and Clustering

R code: R Lecture 10

R Lecture 11: Monte Carlo Simulation and the Bootstrap

R code: R Lecture 11

R Lecture 12: Nonparametric Regression and Cross-Validation

R code: R Lecture 12, Shiny App: Nonparametric Regression

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