I don't suppose you could run that one last time - just restrict the data set to everything newer than June 25, 2005 (I think). That would be version 4, the current game, of MegaMillions.
I'll be the first to admit - statistics was the first math class I actually bombed ... only got a B- and that was years ago. So there are undoubtedly things I did in this analysis that would make a statistician cringe. I'm hoping more that the aggregation of all the different tables will result in something mildly more meaningful that straight random.
What I was looking for in my previous post was, the order and time the numbers were drawn. I wanted to use the analysis calculated by the earth's rotation including leap year minutes (x=(525,600*.25)+525,600) to see if there is indeed an prediction that can be achieved based on moments of time.
I know it is a bit much but I do not believe in randomnimity. I believe the word random was invented to fill in an explaination of things that happen on time intervals that we cannot explain. Thus my ultimate theory is that a number drawn can be predicted at the exact second, the trick is, finding the right calculation. Who knows if the Julian calendar is even the right approach?
I know it is out there and probably deemed cooky, but what if?
-IVX

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RE: Where's the analysis?
I did do some analysis. Compensating for the four (ouch) different types, I get an overall p-value of 0.792. This means there is absolutely NO statistical significance...
You can test it yourself if you have R:
#Get big.dat at http://www.state.nj.us/lottery/data/big.dat big=read.table("big.dat",sep="%",fill=T) big$date=as.Date(apply(big[,1:3],1,paste,collapse="-")) big$type=ifelse(big$date>"1999-1-13",ifelse(big$date>"2002-3-15",ifelse(big$date>"2005-06-22",4,3),2),1) big$maxnorm=c(50,50,52,56)[big$type] big$maxspecial=c(25,35,52,46)[big$type] maxnorms=table(big$maxnorm) p=rep(0,56) for(i in 1:nrow(maxnorms)) p[1:as.numeric(names(maxnorms)[i])]=p[1:as.numeric(names(maxnorms)[i])]+maxnorms[i]*5 maxspecial=table(big$maxspecial) for(i in 1:nrow(maxspecial)) p[1:as.numeric(names(maxspecial)[i])]=p[1:as.numeric(names(maxspecial)[i])]+maxspecial[i] p=prop.table(p) allnum=unlist(big[,5:10]) t=table(allnum) chisq.test(t,p=p) plot(t/p)P
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