Google Docs and The Olympics

I did a graphical analysis of Olympic Medal Distributions here

http://spreadsheets.google.com/ccc?key=pS9vSxWuwOlmcp-m7LUaAKA&hl=en

After ranking the countries, and doing logs of their medals, I did the graphs (they are in sheet 2) – here are the findings

1) Log Bronze Medals is like a step function across countries

2)Total Medals is like a Poisson function.

3) Log Total Medals is like a nice Lorenze curve like figure.

4) Google docs charts is not as good as excel , but they are coming close.

5)Google docs charts are no MS silverlight when it comes to showing online graphs.

But they are ready to be published on the web in 5 mins.

6) if Michael Phelps was a country , he would be number 6 or 7.

7) China leads in gold medals (equal to next three US, Russia and UK combined)

8) US leads in total medals.

9) Worst per capita medal tally (non zero) would be India. 3 Medals per billion people.This is the first time India has won more than 1 medal in an Olympics though. We are still very very very happy about it.

10) Sports and Olympics are cool stuff. Despite the country rankings, most countries love sports.

Number 10 is not based on data but on hope. A special thanks to discussions which inspired this .http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0808C&L=sas-l&D=1&O=D&P=42233

ROC Curve

ROC Curve is a nice modeling concept to know as it will used practically in nearly all models

irrespective of spoefic technique and irrespective of statistical software.

We use the Wikipedia for referring to easy to implement statistics rather than crusty

thick books which seem prohibitely dense and opaque to outsiders

-This is how you define the ROC Curve.

actual value
p n total
prediction
outcome
p’ True
Positive
False
Positive
P’
n’ False
Negative
True
Negative
N’
total P N

true positive (TP)

eqv. with hit
true negative (TN)
eqv. with correct rejection
false positive (FP)
eqv. with false alarm, Type I error
false negative (FN)
eqv. with miss, Type II error
true positive rate (TPR)
eqv. with hit rate, recall, sensitivity
TPR = TP / P = TP / (TP + FN)
false positive rate (FPR)
eqv. with false alarm rate, fall-out
FPR = FP / N = FP / (FP + TN)
accuracy (ACC)
ACC = (TP + TN) / (P + N)
specificity (SPC)
SPC = TN / (FP + TN) = 1 ? FPR
positive predictive value (PPV)
eqv. with precision
PPV = TP / (TP + FP)

Here is a good java enabled page to calculate the ROC Curve.

http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html

And in case any one asks, ROC stands for Receiver Operating Characteristic. ……