Univariate data analysis (2014)

Class date(s):	13 to 23 January 2014
(PDF)	Course slides
Download video: Link (plays in Google Chrome) [343 M]
Download video: Link (plays in Google Chrome) [M]

Class materials

Date	Class number	Video and audio files		Other materials	Reading (PID)	Slides
13 January	02A	Video (343 M)	Audio (42 M)	R demo file	Chapter 2	Slides for class
15 January	02B	Video (327 M)	Audio (42 M)	None
16 January	02C			None

Software source code

Please follow the software tutorial to install and run the course software. You should be able to quickly read, understand and use the material in steps 1 to 13.

Class example, 15 Jan

Seeing the Central Limit Theorem in action: rolling dice.

N = 500
m <- t(matrix(seq(1,6), 3, 2))
layout(m)
s1 <- as.integer(runif(N, 1, 7))
s2 <- as.integer(runif(N, 1, 7))
s3 <- as.integer(runif(N, 1, 7))
s4 <- as.integer(runif(N, 1, 7))
s5 <- as.integer(runif(N, 1, 7))
s6 <- as.integer(runif(N, 1, 7))
s7 <- as.integer(runif(N, 1, 7))
s8 <- as.integer(runif(N, 1, 7))
s9 <- as.integer(runif(N, 1, 7))
s10 <- as.integer(runif(N, 1, 7))

hist(s1, main="", xlab="One throw", breaks=seq(0,6)+0.5)
bins = 8
hist((s1+s2)/2, breaks=bins, main="", xlab="Average of two throws")
hist((s1+s2+s3+s4)/4, breaks=bins, main="", xlab="Average of 4 throws")
hist((s1+s2+s3+s4+s5+s6)/6, breaks=bins, main="", xlab="Average of 6 throws")
bins=12
hist((s1+s2+s3+s4+s5+s6+s7+s8)/8,  breaks=bins, main="", xlab="Average of 8 throws")
hist((s1+s2+s3+s4+s5+s6+s7+s8+s9+s10)/10, breaks=bins, main="", xlab="Average of 10 throws")