Negative_Jeff Snowball, I notice the commencement of your blizzard of statistics coincided with your well publicised recent illness. I wish you a speedy recovery.
DITTO
Gordon Browns job , is up for offers....a new challenge for you sir
by tlcs » 05 Mar 2009 11:34
Negative_Jeff Snowball, I notice the commencement of your blizzard of statistics coincided with your well publicised recent illness. I wish you a speedy recovery.
by Royal Rother » 05 Mar 2009 11:40
Dr HfuhruhurrSnowball
Look, if someone says "You don't understand statistics" you can either agree, or say, "OH YES I DO!" (and then they say "Oh no you don't." or you can make the point you have formal qualifications in statistics, worked for a market research bureau and taught stats for a while at Uni.
Well the problem is that to us real statisticians, your statistics are sometimes embarassing. I get enough sarcasm about making stuff up, without someone claiming to be a statistician making stuff up with statistics in a public domain.
by roberto_11 » 05 Mar 2009 11:54
rhroyal As for the original post on the thread, I'm doing an econometrics module which has a lot on regression this semester and I had a mid term exam on it yesterday, so I'm quite knowledgeable at the moment. Whilst leaving variables out of the OLS estimator (regression line) can result in omitted variable bias and make results less accurate, the change is very rarely significant enough to reverse the trend that the current regression line is showing. Including more relevant variables which are correlated with the currently included variables will normally just show that X1 is not solely responsible for values of Y, and the coefficient of X1 will decrease.
In non-economics speak, the stats on Hunt and Kebe accounting for a lot of assists fails to take into account factors such as form, opposition and surrounding players. These factors, if included, could take some of the credit away from Hunt and Kebe. However an Economics graduate should know that these omitted variables are highly unlikely to have such a great impact that the initial findings that we can discredit the fact that Hunt and Kebe are simply two of our most creative players and best options on the wing. Even if the coefficient, i.e. the level to which Kebe and Hunt are responsible for creating our goals, is considerably lower than it was initially when we include all variables, so long as it is still positive there is evidence that they have been our two most creative players this season. I imagine the value would even be large enough that it could pass a test at the 1% significance level that it was accurate (i.e. we could be 99% sure that Hunt and Kebe had been our most creative players so far this season.)
Snowball's statistics cannot be written off on the basis of omitting certain factors. The truth is this is a football board, do you expect people to come up with a load of complicated equations and formulas before going into hypothesis testing? Of course bloody not. Keep up the stats Snowball. They don't tell the full story, but they certainly tell a significant part of it and start debate.
by Dr Hfuhruhurr » 05 Mar 2009 11:59
Royal RotherDr HfuhruhurrSnowball
Look, if someone says "You don't understand statistics" you can either agree, or say, "OH YES I DO!" (and then they say "Oh no you don't." or you can make the point you have formal qualifications in statistics, worked for a market research bureau and taught stats for a while at Uni.
Well the problem is that to us real statisticians, your statistics are sometimes embarassing. I get enough sarcasm about making stuff up, without someone claiming to be a statistician making stuff up with statistics in a public domain.
Poor. Really really poor.
What's been made up?
by Hoop Blah » 05 Mar 2009 12:05
Royal Rother Poor. Really really poor.
What's been made up?
by Ian Royal » 05 Mar 2009 12:50
by roberto_11 » 05 Mar 2009 13:06
by Snowball » 05 Mar 2009 13:12
Ian Royal
Apparently it's ok for him to ignore cup games (when it pleases him) but it's not okay for me to use them at all.
There is no why, there is simple statige
by Snowball » 05 Mar 2009 13:13
roberto_11 So to summarise, hes a retard
by Snowball » 05 Mar 2009 13:14
Dr Hfuhruhurr
His THIS IS NOT A SAMPLE is a classic example of someone trying to fool you.
by roberto_11 » 05 Mar 2009 13:23
by Snowball » 05 Mar 2009 13:27
roberto_11 I note that you havnt actually respnded to my intelligent post earlier on this thread about Kebe and Hunt...
by roberto_11 » 05 Mar 2009 13:43
by loyalroyal4life » 05 Mar 2009 14:01
by roberto_11 » 05 Mar 2009 14:05
by sheshnu » 05 Mar 2009 15:06
roberto_11 Andy Hughes put effort in but couldnt cross a ball for shit.
by Skyline » 05 Mar 2009 15:45
SnowballMr Angry Statistics prove nothing; and relying on them is foolish.
I'll give an example - if a team scored 46 goals in the whole season, they would, statistically at least, be towards the bottom of the goal scoring charts BUT if those 46 goals had resulted in 46 1-0 wins, that makes a mockery of that statistic.
In other words, its about context. A final thought - knowledge is knowing that a tomato is a fruit; wisdom is knowing that you don't put tomatoes into a fruit salad.
No, you are wrong. Goal-scoring is NOT a good predictor. It's goal SUPERIORITY that predicts.
by Dr Hfuhruhurr » 05 Mar 2009 16:18
by Snowball » 05 Mar 2009 16:34
Skyline Goal superiority predicts nothing. It's a reasonable guide to which is the better team, but not a predictor by any means.
Imagine a team that wins half its games 3-0 and loses the other half 1-0. GF=69, GA=23. GD=46. Points = 69. Identical goal diff to Mr Angry's hypothetical team, but 23 points fewer.
by Snowball » 05 Mar 2009 16:47
Dr Hfuhruhurr *
You are using statistics to show Shane is better than Leroy. (As it happens, I agree, but I don’t believe in your applications of stats). You have taken one set of 7 games for Shane, and another set of 7 games for Leroy calculated a number of statistics, and used it to conclude Shane is better than Leroy. It’s the conclusion part that requires an understanding of sampling. So lets give it a go. Your comparison to the general election is valid to a point. There is no disputing that Labour gained more seats than the conservatives at the last election. It’s a fact – such as the facts that you have chosen to compare striking ability between Shane and Leroy in your chosen seven games.
No disagreement here. (Although it is worth pointing out that at the last general election, the Tories and the Conservatives were fighting for seats in the same country, but Shane and Leroy’s statistics were gathered from different games (It would have been far more interesting if it was the same seven games)) But where do we go from here, Oh yes, Shane is better than Leroy and that we should favour Shane over Leroy. This is where your population definition disappears and when you make conclusions or predictions, then what you call populations are actually samples. What are we sampling from? Well the population doesn’t actually exist, but it would need to be the same for Leroy and Shane to make their stats comparable (as your 7 games vs 7 games obviously aren). So we make a population up, which exists purely for us to apply a variation to Shane’s and Leroy’s statistics, so we can estimate what it would be if Leroy had played in Shane’s games and vice versa, or what Leroy’s statistics would be if he played another seven games, or Shane’s, or whatever. Most of your criticisms are that your ‘populations’ don’t compare. Using your definition of population, that is blatantly obvious. Lita’s 7 games are different to Long’s 7 games. If you start accepting that those 7 games are in fact samples from the same population, and embrace the variation this produces (or even just recognise there is some variation to your statistic when you use it for any form of prediction) then your statistics will be more purposeful.
Statistics just isn’t as black and white as you are presenting it. As it happens, and I repeat for emphasis, I do think Shane is better than Leroy and am happy to see statistics supporting this, but there is nothing definite about what those statistics prove or predict.
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