Category: stats Page 2 of 7

Teaching linear models

On 2014/01/02

I teach several courses every year and the most difficult to pull off is FORE224/STAT202: regression modeling.

The academic promotion application form in my university includes a section on one’s ‘teaching philosophy’. I struggle with that part because I suspect I lack anything as grandiose as a philosophy when teaching: as most university lecturers I never studied teaching, although I try to do my best. If anything, I can say that I enjoy teaching and helping students to ‘get it’ and that I want to instill a sense of ‘statistics is fun’ in them. I spend quite a bit of time looking for memorable examples, linking to stats in the news (statschat and listening the news while walking my dog are very helpful here) and collecting data. But a philosophy? Don’t think so.

Statistics unplugged

By Luis

On 2013/12/27

In rblogs, stats, teaching

How much does statistical software help and how much it interferes when teaching statistical concepts? Software used in the practice of statistics (say R, SAS, Stata, etc) brings to the party a mental model that it’s often alien to students, while being highly optimized for practitioners. It is possible to introduce a minimum of distraction while focusing on teaching concepts, although it requires careful choice of a subset of functionality. Almost invariably some students get stuck with the software and everything goes downhill from there; the student moved from struggling with a concept to struggling with syntax (Do I use a parenthesis here?).

I am a big fan of Tim Bell’s Computer Science Unplugged, a program for teaching Computer Science’s ideas at primary and secondary school without using computers (see example videos).

Flotsam 13: early July links

By Luis

On 2013/07/10

In flotsam, python, r, rblogs, stats

Man flu kept me at home today, so I decided to do something ‘useful’ and go for a linkathon:

Ed Yong discusses the effect of subject expectations in psychology experiments Nice Results, But What Did You Expect? At the beginning there was another article on The placebo phenomenon, and another one on The placebo defect.
A googleVis tutorial to create Hans Rosling-type graphs from R.
Google’s Python Class is material for an intensive 2-day course on Python.
An opinion piece on Calculus and statistics by Daniel Kaplan, on teaching a different version of your typical introductory calculus course, so it is useful for statistics. He goes as far as teaching calculus using R. There is more information in Project MOSAIC.
Nice graphs on what happened to Asiana Airlines flight 214. I didn’t know there was so much available data for a specific flight.
Biased and Inefficient, Thomas Lumley’s personal statistics blog (he insists that posting 75% of Statschat is not enough to qualify as personal). You may know Thomas from the survey package (or a few others).
If you are a postgrad student in New Zealand you can apply for a NeSI (New Zealand eScience Infrastructure) postgraduate allocation to access high performance computing facilities.
My previous post the USA versus Western Europe comparison of GM corn was the first time that I received more traffic from Facebook than from R-bloggers. Five hundred readers in total.

Over and out.

My take on the USA versus Western Europe comparison of GM corn

By Luis

On 2013/07/04

In data curious, genetically modified, r, rblogs, stats, teaching

A few days ago I came across Jack Heinemann and collaborators’ article (Sustainability and innovation in staple crop production in the US Midwest, Open Access) comparing the agricultural sectors of USA and Western Europe^‡. While the article is titled around the word sustainability, the main comparison stems from the use of Genetically Modified crops in USA versus the absence of them in Western Europe.

I was curious about part of the results and discussion which, in a nutshell, suggest that “GM cropping systems have not contributed to yield gains, are not necessary for yield gains, and appear to be eroding yields compared to the equally modern agroecosystem of Western Europe”. The authors relied on several crops for the comparison (Maize/corn, rapeseed/canola^{see P.S.6}, soybean and cotton); however, I am going to focus on a single one (corn) for two reasons: 1. I can’t afford a lot of time for blog posts when I should be preparing lectures and 2. I like eating corn.

GM-fed pigs, chance and how research works

By Luis

On 2013/06/21

In data curious, genetically modified, stats, teaching

Following my post on GM-fed pigs I received several comments, mostly through Twitter. Some people liked having access to an alternative analysis, while others replied with typical anti-GM slogans, completely ignoring that I was posting about the technical side of the paper. This post is not for the slogan crowd (who clearly are not interested in understanding), but for people that would like to know more about how one would evaluate claims from a scientific article. While I refer to the pig paper, most issues apply to any paper that uses statistics.

In general, researchers want to isolate the effect of the treatments under study (diets in this case) from any other extraneous influence. We want control over the experimental conditions, so we can separate the effects of interest from all other issues that could create differences between our experimental units (pigs in this case). What could create ‘noise’ in our results? Animals could have different genetic backgrounds (for example with different parents), they could be exposed to different environmental conditions, they could be treated differently (more kindly or harshly), etc.