Home > STAT 873
Day #1
Section 1 - Background material
- Introduction to R:
Notes,
InitialExamples.R,
GPA.R,
gpa.txt,
gpa.xls,
gpa.csv,
diamond_prices.xls,
Homework
- Matrix algebra: Notes,
basic_matrix_algebra.r,
HS_college_GPA_MA.r,
gpa_MA.txt,
Homework
- Data-distributions-correlation:
Notes,
cereal.r,
cereal.xls,
cereal.csv,
normal_plot.r,
Homework
- Please remember to examine the tests from 2013
and to reproduce all examples in the notes
Section 2 - Summarizing data
- Graphics: Notes,
cereal_graphics.r,
sim_results.xls,
Homework
- Principal component analysis: Notes,
cereal_PCA.r,
goblet_PCA.r,
goblet.csv,
Homework
- Factor analysis: Notes,
MLE supplement,
goblet_FA.r,
LikelihoodFunction.r,
Homework,
police_applicant.csv
- Cluster analysis: Notes,
dist_example.R,
goblet_CA.r,
two_var.r,
Homework
- Please remember to examine the projects and tests from 2013
and to reproduce all examples in the notes
Section 3 - Prediction
- Discriminant analysis: Notes,
Placekick_DA.r,
factors.r,
Placekick.csv,
valid.csv,
Homework
- Nearest neighbor analysis: Notes,
4obs.r,
Placekick_NNC.R,
Homework
- Logistic regression:
Notes,
accuracy_plot.r,
Non-convergence.R,
Placekick_logisticreg.R,
PiPlot.R,
Swets et al. (2000),
Homework
- Multinomial regression:
Notes,
wheat_mult_reg.r,
MultinomialModelPlot.r,
plot_all.r,
wheat_all.csv,
Homework,
turkey.csv
- Random forests
- Please remember to examine the projects and tests from 2013
and to reproduce all examples in the notes
Section 4 - Classical methods