Posts Tagged ‘ teaching ’

Introduction to Bayesian lecture: Accompanying handouts and demos

October 19, 2012
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Introduction to Bayesian lecture: Accompanying handouts and demos

I recently posted the slides from a guest lecture that I gave on Bayesian methods for biologists/ecologist. In an effort to promote active learning, the class was not a straight forward lecture, but rather a combination of informational input from me and opportunities for students to engage with the concepts via activities and discussion of

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Introduction to Bayesian Methods guest lecture

October 18, 2012
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Introduction to Bayesian Methods guest lecture

This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides

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Quantifying student feedback using Org mode and R

September 30, 2012
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Quantifying student feedback using Org mode and R

As the term has progressed, my LSM2241 lectures are getting more consistent. I’m aiming to use 45 slides for what is officially a two hour lecture, although in reality it lasts about 90 minutes. We take a break at about 50 … Continue reading →

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Some regressions on school data

September 26, 2012
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Some regressions on school data

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 … Continue reading →

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Why we are teaching massive open online courses (MOOCs) in R/statistics for Coursera

August 10, 2012
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Editor’s Note: This post written by Roger Peng and Jeff Leek.  A couple of weeks ago, we announced that we would be teaching free courses in Computing for Data Analysis and Data Analysis on the Coursera platform. At the same time, a number of ot...

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Careless comparison bites back (again)

August 6, 2012
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Careless comparison bites back (again)

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 … Continue reading →

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

July 30, 2012
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Split-plot 2: let’s throw in some spatial effects

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 … Continue reading →

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Dynamical systems: Mapping chaos with R

July 13, 2012
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Dynamical systems: Mapping chaos with R

Chaos. Hectic, seemingly unpredictable, complex dynamics. In a word: fun. I usually stick to the warm and fuzzy world of stochasticity and probability distributions, but this post will be (almost) entirely devoid of randomness. While chaotic dynamics are entirely deterministic, their sensitivity to initial conditions can trick the observer into seeing iid. In ecology, chaotic

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Split-plot 1: How does a linear mixed model look like?

June 24, 2012
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Split-plot 1: How does a linear mixed model look like?

I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a split-plot, particularly because ‘it didn’t look like’ a split-plot to … Continue reading →

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Review: “Forest Analytics with R: an introduction”

May 29, 2012
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Review: “Forest Analytics with R: an introduction”

Forestry is the province of variability. From a spatial point of view this variability ranges from within-tree variation (e.g. modeling wood properties) to billions of trees growing in millions of hectares (e.g. forest inventory). From a temporal point of view … Continue reading →

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