ARE 210: Probability and Statistics
University of California, Berkeley
Course information
Lecturer: Charlie Gibbons [homepage
 email]
Lecture: Mondays and Wednesdays, 56:30pm
Location: Giannini 201
My office hours: After class (Giannini 234) or by appointment
Syllabus
Announcements
The first day of class is Wednesday, August 26.
The takehome midterm will become available on Monday, October 26 at 5pm and will be due at the start of lecture on Wednesday, October 28.
Assignments and exams
Problem set 1 [solutions
R code  knitr]
Problem set 2 [solutions]
Problem set 3 [
solutions]
Problem set 4 [
solutions part 1 
solutions part 2]
Midterm exam [
solutions]
Problem set 5 [data 
solutions]
Problem set 6 [solutions]
Final exam [data  solutions]
Handouts
Introduction to Probability Theory [slides
 notes  simulation]
Conditional Probability and Bayes' Rule [slides
 notes]
Random Variables [slides
 notes]
Probability Models in Econometrics [slides
 notes]
Expectations [slides
 notes]
Rubin Causal Model [slides
 notes]
Limiting Distributions [slides
 notes
 code]
Point Estimation [slides
 notes
 code]
Maximum Likelihood Examples
Autoregressive Models [slides
 notes]
Sampling Techniques [slides
 notes]
Parametric Statistical Inference [slides
 notes]
Bootstrapping [slides
 notes]
NonNested Likelihood Ratio Test: A Bootstrapping Example
[data]
Fisher's Exact Test and its Extensions [slides
 notes]
Bayesian Analysis [slides
 notes]
Other files
Simulation in R
Beamer example
R resources
A Video Introduction to R courtesy of Google
Notes
on R: A Programming Environment for Data Analysis and Graphics
R
quick reference card
Econometrics
in R
Google's
R Style Guide
R Programming Wikibook
RStudio
Emacs and ESS
R in a Nutshell
by Joseph Adler
R
Inferno (see, especially, The Eighth Circle)
Stack Overflow's
R Q&A board
ggplot2 Reference Manual
knitr (to link R and LaTeX)
Matlab/R
Reference Guide
My teaching materials are licensed under a
Creative Commons AttributionNoncommercial 3.0 United States License.
