Maryclare Griffin

Teaching

Below are names of classes I am teaching/have taught, a recent syllabus, and notes. Here are some links that are generally helpful for students taking a class with me:

• R Markdown Reference Guide

Statistical Computing

• Syllabus
• Typed Notes:
  • Coding, IDE (.pdf, .qmd)
  • Modes, Structures (.pdf, .qmd)
  • Random Variables (.pdf, .qmd)
  • Functions (.pdf, .qmd)
  • Data (.pdf, .qmd)
  • Big Data (.pdf, .qmd)

Problem Sets:

• Due 9/17 (.pdf, .qmd)
• Due 9/24 (.pdf, .qmd)
• Due 10/1 (.pdf, .qmd)

• Due 10/15 (.pdf, .qmd)

• Misc:
  • Demo (.pdf, .qmd)
  • Smoothing (.R)
  • Making an AR Covariance (.R)
  • Middle Square Method (.R)
  • .csv Examples (sportsref_download_1.csv, sportsref_download_2.csv, sportsref_download_3.csv, sportsref_download_4.csv)
  • Other Data Examples (sportsref_download_5.txt, sportsref_download_6.tsv, sportsref_download_7.txt, sportsref_download_8.txt)
  • Excel Data Example (sportsref_download.xls)
  • Reading in Data (.R)

Statistical Learning

• Syllabus
• Typed Notes:
  • Overview (.pdf, .Rmd)
  • Statistical Learning (.pdf, .Rmd)
  • Regression (.pdf, .Rmd)
  • Classification (.pdf, .Rmd)

Time Series

• Syllabus
• Typed Notes:
  • Introduction
  • Basic Time Series Concepts
  • ARMA Models
  • Forecasting
  • Estimation and Selection
  • Non-Stationarity
  • State Space
  • Stochastic Volatility
  • Spectral Methods/The Spectral Domain
  • Multivariate Regression
  • Multivariate Time Series Concepts
  • Multivarate Autoregressive Moving Average Models

Linear Regression

• Syllabus
• Typed Notes:
  • Notes 1 (.pdf, .Rmd)
  • Notes 2 (.pdf, .Rmd)
  • Notes 3 (.pdf, .Rmd)
  • Notes 4 (.pdf, .Rmd)
  • Notes 5 (.pdf, .Rmd)
  • Notes 6 (.pdf, .Rmd)
  • Notes 7 (.pdf, .Rmd)
  • Notes 8 (.pdf, .Rmd)
  • Notes 9 (.pdf, .Rmd)
  • Notes 10 (.pdf, .Rmd)
  • Notes 11 (.pdf, .Rmd)
  • Notes 12 (.pdf, .Rmd)
  • Notes 13 (.pdf, .Rmd)
• Book Data:
  • Dwaine
  • Plasma
  • Plutonium
  • Toluca