Section 0 - Introduction, R

• Notes
  • Syllabus.pdf
  • Introduction to R: 1-RintroBasics.docx (video)
  • Program editors for R: 2-ProgramEditors.docx (video)
• MathType
  • EquationFonts.docx
  • mtextra.ttf
• R programs
  • AppendixInitialExamples.R
• Please remember to reproduce all examples in the notes

Section 1 - Core

• Notes
  • Introduction to regression: 1-Introduction.docx, PlotFunctionArguments.docx
  • Least squares method: 2-LeastSquares.docx
  • Object oriented language: 3-ObjectOrientedLanguage.docx
  • Multiple linear regression: 4-MultipleLinearRegression.docx
  • Matrix algebra: 5-MatrixAlgebra.docx
  • Unbiased: 6-Unbiased.docx
  • Variability: 7-Variability.docx
  • Inference:
  • Data generation:
• Data
  • gpa.csv
  • gpa_pizza.csv
• Excel
  • Least_squares_demo.xlsx
• R programs
  • BasicMatrixAlgebra.R
  • gpa.R
  • MSE.R
  • TwoExplanatoryVariables.R
• Please remember to reproduce all examples in the notes

Section 2 - Model refinements

• Notes
  • Interactions:
  • Transformations:
  • Categorical explanatory variables:
  • Diagnostics part 1:
  • Diagnostics part 2:
  • Model selection:
• Data
• R programs
• Please remember to reproduce all examples in the notes

Section 3 - ANOVA

• Notes
  • Completely randomized designs:
  • Randomized complete block designs:
  • Multiple comparisons:
• Data
• R programs
• Please remember to reproduce all examples in the notes