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{ Author Archives }

Graduation day!

Last week Lara Maspoli, Suzanne Greene, and Rich Baxter (MSc) and Gian Marco Palamara (PhD) received their  Diplomas. Congratulations to you all from the rest of the Petchey-Hansen group! Below are some pictures from the ceremony. Notice how the tense faces ease into broad smiles as the ceremony proceeds…

Testing the Metabolic Theory of Ecology: a simple pipeline using R.

[Download the simulated data and the script used in the following example] Many biological variables depend on the size of the organisms and on the environmental temperature. For example, large organisms tend to grow more slowly, and live longer, than small ones. On the other hand, organisms tend to grow faster in warm climates compared […]

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About p-values

I stepped into a blog post by Pia Parolin titled “Do all biological processes need to be statistically significant?” (http://tinyurl.com/nwcq5xa). It sounds, at moments, the frustrated cry of the field biologist observing cool patterns and building cool theories on it until he/she faces that bloody p-value=0.051. Who hasn’t been there? Yet Pia’s article contains more […]

Some notes on Principal Component Analysis

Principal Component Analysis (PCA) is an ordination method that reduces the dimensionality of multivariate data by creating few new key explanatory variables called principal components (PCs). Each PC accounts for as much variance in the data as possible, provided that all the PAs are uncorrelated: therefore all PCs are independent and orthogonal. It is possible […]

The art of advertising science: “elevator pitches”

As scientists we can underestimate the importance of communicating what we do; this does not only mean publishing specialistic articles, but also sharing our findings with the non-specialistic public. After all, science becomes knowledge only when shared. Also, I like to think that tax-funded researchers have a responsibility toward who funds them (i.e. any tax […]

How to read ANCOVA summary tables in R

I don’t know about you, but as soon as models get slightly complex I keep forgetting how to read R’s summary tables properly. I mean, the first lines are easy, but what about the weird interacting effects? I know, it is quite embarassing. I put an example here, pro memoria for me and for you […]

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