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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 Classification

Publication Date

Fall 2016

City

Albuquerque

Keywords

data visualization, classification, model checking

Disciplines

Applied Statistics | Design of Experiments and Sample Surveys | Multivariate Analysis | Statistical Methodology | Statistical Models

Advanced Data Analysis - Lecture Notes

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