This course will provide hands-on learning of basic statistics and probability using the interactive R programming language. Enrollment will be limited to 11 to allow close interaction between students and the instructor and foster a supportive environment for those with limited computing background. Besides grounding theory in a concrete, applied setting, knowledge of "R" will provide a practical skill students can subsequently apply for analyzing quantitative information arising within their particular area of study or interest.

Handouts

• 1/20/10: Lab 1
• 1/27/10: Lab 2
• 2/03/10: Lab 3
  • Assignment 3
  • Notes on marginal and conditional probabilities (2/8/10)
  • Probability chapter: see 1.2, 1.3, & 1.5 (see also: slides)
• 2/10/10: Lab 4
• 2/24/10: Lab 5
• 3/03/10: Lab 6
• 3/10/10: Lab 7
  • Notes on Bernoulli and Binomial expectation and variance
• 3/24/10: Lecture Notes
• 3/31/10:
  • Lab 8
  • Assignment 9
• 4/14/10:
  • Lab 9
  • Lecture slides: 1 2 3
• 4/21/10: Lab 10
• 4/27/10: Lab 11
• 5/05/10: Lab 12

Course Project: you are free to choose, but here are some ideas

• examine the relation between words used in product reviews and product scores: data
• Datamob data
• UCI Machine Learning data

Course Textbook: John Verzani, Using R for Introductory Statistics, 2004

• Our library has an ebook version which has different layout but the SAME content (publisher has confirmed). Page numbers may differ.
• Supporting website
• Errata (only 6 pages, be sure to refer to this for each chapter)
• Answers to selected problems

Resources

• Download R for your personal computer: OS X and Windows.
• R website
• R wiki
• Handy R reference card
• R Language Definition
• Verzani's earlier simpleR -- Using R for Introductory Statistics (searchable PDF)
• Manual "An Introduction to R": HTML PDF
• R for Matlab users: guide

Highlights

• Statistics: mean, variance, standard deviation, quartiles and quantiles, correlation (Pearson, Spearman), central limit theorem, confidence intervals, analysis of variance, regression models, sampling, significance testing, plots and charts
• Probability: discrete and continuous distributions, marginals, conditionals, parametric families, expectation
• Programing: variables, fuctions, control flow, scope, ...