Expert Bias and Making a Screencast 'better'

I've updated the screencast tutorial for EPR. Even if I augmented the quality of the recording, I have the bad feeling that I've complicated the explaination. Dan and Chip Heaths "Made to Stick" argues that experts fail to teach, because they focus too much on 'important' details. So eventually I have to do it again.

How to Solve It! A book review

"Schule des Denkens"  means school of thought. The title of the original manuscript reveals the motivation for this book: to teach better. George Polya wrote it as a guide on how to lecture mathematics. But only the publicized edition, after Polya's migration to the United States, tells in plain English why its relevant for a broader audience: How to Solve It. I am not a teacher, the didactic musings of a lecturer would produce nothing more than a spark of curiosity in me. But "How to Solve It" describes a general procedure for problem solving. This freaks me out! Polya hides this endeavor in favor of the didactic justification, and only later in this book he will reveal that it is about a Modern Heuristic, to "understand the process of solving problems, especially the mental operations typically useful in this process" [p. 129]. The book is divided into four parts, I will discuss them briefly:

Part I: In the Classroom

This 32 pages are all you need to grasp his algorithm of problem solving. Good ideas are simple and the procedure proposed is not counterintuitive. You could easily come a similiar conclusion by your own:

What is the first step of problem solving? 

(1) Understanding the problem. 

What is the next step? 

(2) Devise a plan. 

Then?

(3) Execute the plan. 

And finally

(4) Look back

Mightily impressed? Then you are a lobotomized PowerPoint disciple! But follow Polya in a Socratic dialog with the classroom and look into the train of thoughts of an educated problem solver. There are many subtilities to discover.

By reading this chapter, more than once I had moments of Heureka!, when Polya guides you to ask the so called right questions and instructs you how to take a different point of view of the problem.

Part II: How to Solve it - a Dialog

The second part compresses the problem solving procedure, the ars inveniendi, in a summary of two pages. I did not gain from this, but it might be helpful as a short rehearsal when time passes by.

Part III: Dictionary of Heuristic

This is a 200 pages collection of heuristics to use as a pattern language for problem solving. The autor advices to take your time read this piece by piece when you are struggling with problems. Which I do.

Part IV: Problems, Hints, Solutions

These 8 pages are filled with exercises and smart hints how to approach the individual problems.

Conclusions

Polya opens your mind for solutions. I will tackle future hard problems only with Polya's algorithm and benefit from the careful order he imposes to the confused mind.

Albeit written for teaching mathematics, I suspect that Polyas work is useful not only for quantitativ problem solving, but for qualitative problems too. Here, I have no proof and only the application of it will tell. 

The book is easy to read, and you might master the first fundamental part "In the Classroom" in 3-5 hours. It is also a really cheap book, 13-something Euros, and if you are a problem solver you will need and enjoy it.

Butter in Bangladesh as a S&P 500 predictor

http://nerdsonwallstreet.typepad.com/my_weblog/files/dataminejune_2000.pdf

Fully Automated Prediction with Random Forests

Put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree "votes" for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Basically Random Forests automatically generate many decision trees with mostly weak predictive goodness, and gain high predictive power by averaging them out. The algo can be sketched like this:

1. randomly sample variables and predictors, repeat:
1. identifying a predictor
2. repeat down the tree
1. seeking the most correlated variable
2. make a binary decision out of it
2. combine all predictions and average them out, voila!

Result: high predictive goodness sans parameters!

http://stat-www.berkeley.edu/users/breiman/RandomForests/

Switching of the Nuclear Powerplants in Germany now?

Today chancellor Merkel announced to switch of  7 nuclear power plants as a consequence of the fukushima catastrophe.
The decision means cutting 10% to 15% of Germanys power consumption, using the net power production median of the weakest nuclear powerplants (890 MW) and the median of the overall powerplants (1288 MW)

Update: they named the plants in question


de.wikipedia.org/wiki/Liste_der_Kernreaktoren_in_Deutschland

Unterweser

KKU

NI

E.ON

1.410

1.345

Biblis B

KWB B

HE

RWE

1.300

1.240

Biblis A

KWB A

HE

RWE

1.225

1.167

Philippsburg 1

KKP 1

BW

EnBW

926

890

Isar/Ohu 1

KKI 1

BY

E.ON

912

878

Neckarwestheim 1

GKN 1

BW

EnBW

840

785

Brunsbüttel

KKB

SH

Vattenfall

806

771

These have a net power of 771+785+878+890+1167+1240+1345= 7076 MW or 11.3% of annual consumption.

Update: the last data on power trade balance shows a surplus of 22 TWh

Hot Bozen

Since I study time series analysis, here a little exercise with data from Hydrographisches Amt South Tyrol:

Its getting hotter:
decomposing mean temperature in Bozen 1981-2010, 
the observed data is splitted into a seasonal component and a level

The code for R:

bz<-read.csv("data/bozen.csv.txt", header=FALSE)

bz<-ts(bz[,1], start=c(1981,1), frequency = 12)

plot(decompose(bz))

+2°C is the mean temperature increase since 1971

R-Quote of the Day

"It is not meant to be the big data distributed crunching engine; it is more like the intellectual's answer to Excel."  - such a post, and the day begins with a smile.