Lecture notes for Advanced Data Analysis (ADA1 Stat 427/527 and ADA2 Stat 428/528), Department of Mathematics and Statistics, University of New Mexico, Fall 2016-Spring 2017. Additional material including RMarkdown templates for in-class and homework exercises, datasets, R code, and video lectures are available on the course websites: https://statacumen.com/teaching/ada1 and https://statacumen.com/teaching/ada2 .
Contents
I ADA1: Software 0 Introduction to R, Rstudio, and ggplot
II ADA1: Summaries and displays, and one-, two-, and many-way tests of means 1 Summarizing and Displaying Data 2 Estimation in One-Sample Problems 3 Two-Sample Inferences 4 Checking Assumptions 5 One-Way Analysis of Variance
III ADA1: Nonparametric, categorical, and regression methods 6 Nonparametric Methods 7 Categorical Data Analysis 8 Correlation and Regression IV ADA1: Additional topics 9 Introduction to the Bootstrap 10 Power and Sample size 11 Data Cleaning
V ADA2: Review of ADA1 1 R statistical software and review
VI ADA2: Introduction to multiple regression and model selection 2 Introduction to Multiple Linear Regression 3 A Taste of Model Selection for Multiple Regression
VII ADA2: Experimental design and observational studies 4 One Factor Designs and Extensions 5 Paired Experiments and Randomized Block Experiments 6 A Short Discussion of Observational Studies
VIII ADA2: ANCOVA and logistic regression 7 Analysis of Covariance: Comparing Regression Lines 8 Polynomial Regression 9 Discussion of Response Models with Factors and Predictors 10 Automated Model Selection for Multiple Regression 11 Logistic Regression
IX ADA2: Multivariate Methods 12 An Introduction to Multivariate Methods 13 Principal Component Analysis 14 Cluster Analysis 15 Multivariate Analysis of Variance 16 Discriminant Analysis 17 Classificationhttps://digitalrepository.unm.edu/unm_oer/1002/thumbnail.jp
