Open Windows

Screenshot of my windows task bar.

I particularly like the three icons on the top. Each of those represents a terminal that connects me to one of the many GNU screens that help me keep myself organized. Each of these GNU screens has up to 10 tabs. I also have about 50+ tabs open in Firefox right now. A sign that Firefox hasn't crashed in a while ;-)

I'm also particularly excited about having the 2 Ps and the one R window open. It's been a while since the last time.

Oh, and if TweetDeck wouldn't constantly crash on me I'd have that open too.

My top 20 artists in 2009 so far

Size of the artist images corresponds to number of times I listened to each.

Webservice opening hours

British Gas sent me an email and requested me to read my gas-meter and submit the reading via their web page.

I tried and failed. Their website tells me:

"Access to the application is currently unavailable. If the time is between 6am - 11pm Monday to Friday, 6am - 6pm Saturday or 8am - 11pm Sunday please try again in few minutes. Otherwise, please try again between the times outlined above."

Which reminds me of HSBC which only allows Internet banking money transfers within opening hours.

I wonder if what happens is:
(1) web form is submitted by user (=me)
(2) web form is sent to the printer at British Gas or HSBC
(3) Britsh Gas or HSBC employee gets form from printer and types it into computer

Hanami Season

As seen on my way to work today:



Not comparable to other stuff I've seen but nice nevertheless :-)

Twitter

This video and many discussion with my colleagues convinced me. I need to learn what twitter is about.

Abusing Simple Linear Regression Models

I was very excited about the book I recently bought. However, after a brief first read I'm not sure I'll continue reading it.

Here are two examples of "evidence" presented in the book.



There is a lot to be said about this. For example, that the regression line is obviously a bad fit for the data.

A very quick and rough analysis in Matlab yields the following:



The red line is the regression line the authors use. The blue line is the regression line you get if you drop a single data point (in particular the US). The model the authors use suggests that the homicides go from 10 (low inequality) to about 30 (high inequality) per million (a 200% increase). Dropping the US results in a model that predicts 13 to 22 (an increase of less than 100%). The authors don't bother to point out any of this.

Furthermore, the (95% confidence level) prediction boundaries (dotted lines) indicate that the models need to be treated carefully. The confidence levels for predictions range up to 34 for low inequality and down to 6 for high inequality. The overlap suggests that the authors should gather more data if they want to prove that "Homicides are more common in more unequal countries".

The second example:



The authors briefly mention that there is an issue when using only so few data points. On the other hand they pretend that the data is very clear. Removing the UK and US will completely change the results of their regression model. The data gives their conclusion ("Social mobility is lower in more unequal countries") only very weak support. I also don't like how they scaled it. If I assume that the "low-high" ranges are comparable in both cases I would expect this to be reflected in the plot. Instead the mobility dimension seems visually about 30% less important than the inequality dimension. Anyway, I'm not really sure how useful "low" and "high" without exact definitions are in the first place.

Spring Equinox Celebrations

Victoria park is just around the corner from where we live.





The above image shows my bike on the left. I recently brought it to a repair shop to have some stuff repaired. At first they refused because they thought the tube which got damaged in an accident I had a long time ago could fall apart any moment. I had to go there a second time to convince them that I'd be fine.