Day #1

• Office location
• Syllabus; syllabus supplement
• Everitt, B. and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. Springer. http://library.unl.edu/record=b4156441
• Hewson, P. (2009). Multivariate Statistics with R.
• Blended learning
• Listserv_help

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

• Testing means: Notes, CPT.r, hotelling_sim.r
• Please remember to reproduce all examples in the notes