Category: teaching (Page 11 of 14)

Some regressions on school data

2012-09-26 / Luis

Eric and I have been exchanging emails about potential analyses for the school data and he published a first draft model in Offsetting Behaviour. I have kept on doing mostly data exploration while we get a definitive full dataset, and looking at some of the pictures I thought we could present a model with fewer predictors.

The starting point is the standards dataset I created in the previous post:

Suicide statistics and the Christchurch earthquake

2012-09-06 / Luis

Suicide is a tragic and complex problem. This week New Zealand’s Chief Coroner released its annual statistics on suicide, which come with several tables and figures. One of those figures refers to monthly suicides in the Christchurch region (where I live) and comes with an interesting comment:

Suicides in the Christchurch region (Timaru to Kaikoura) have risen from 67 (2010/11) to 81 (2011/12). The average number of suicides per year for this region over the past four years is 74. The figure of 67 deaths last year reflected the drop in suicides post-earthquake. The phenomenon of a drop in the suicide rate after a large scale crisis event, such as a natural disaster, has been observed elsewhere. [my emphasis]

Gratuitous picture: horse race in the beach (Photo: Luis).

m x n matrix with randomly assigned 0/1

2012-08-29 / Luis

Today Scott Chamberlain tweeted asking for a better/faster solution to building an m x n matrix with randomly assigned 0/1. He already had a working version:

r <- 1000
c <- 1000
m0 <- matrix(0, r, c)
apply(m0, c(1,2), function(x) sample(c(0,1),1))

Gratuitous image: Tree spread on metal frame to provide shade in a plaza, Lisbon, Portugal. Some days I would love to have a coffee there without computer, just watching the world pass by. (Photo: Luis).

Careless comparison bites back (again)

2012-08-07 / Luis

When running stats labs I like to allocate a slightly different subset of data to each student, which acts as an incentive for people to do their own work (rather than copying the same results from a fellow student). We also need to be able to replicate the results when marking, so we need a record of exactly which observations were dropped to create a particular data set. I have done this in a variety of ways, but this time I opted for code that looked like:

setwd('~/Dropbox/teaching/stat202-2012')

biom <- read.csv('biom2012.csv', header = TRUE)
drops <- read.csv('lab4-dels.csv', header = TRUE)

# Use here your OWN student code
my.drop <- subset(drops, student.code == 'mjl159')
my.data <- subset(biom, !(id %in% my.drop))

Gratuitous picture: Detail of Mosteiro dos Jerónimos, Belém, Lisboa, Portugal (Photo: Luis).

Split-plot 2: let’s throw in some spatial effects

2012-07-31 / Luis

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon.

Where was I? Showing how split-plots look like under the bonnet (hood for you US readers). Yates presented a nice diagram of his oats data set in the paper, so we have the spatial location of each data point which permits us playing with within-trial spatial trends.
Continue reading