IBTG81 Posted November 9, 2006 Share Posted November 9, 2006 I smell a telephone pole... 829482[/snapback] What about you smelling poles? Link to comment Share on other sites More sharing options...
Ramius Posted November 9, 2006 Share Posted November 9, 2006 I suggest you reread the bolded sentence in the quote from my above post.Regression toward the mean occurs any time people are chosen based on observed scores that are determined in part or entirely by chance. The element of chance--that is, of measurement error--causes scores to seem more widely distributed than they really are. Thus, someone who scored a 750 on the math section of the SAT is expected to get a 725 upon retaking that test. 830319[/snapback] my God you are a complete fuggin moron. You still dont even understand what the hell you are blindly copying/pasting. Link to comment Share on other sites More sharing options...
Orton's Arm Posted November 9, 2006 Share Posted November 9, 2006 my God you are a complete fuggin moron. You still dont even understand what the hell you are blindly copying/pasting. 830329[/snapback] On the contrary, the Hyperstat example I found is exactly what I've been trying to explain for the last untold number of pages. Look again at my I.Q. test examples (which I wrote before I found that page I linked to). I described how someone who scored a zero on an I.Q. test would, upon retaking it, be expected to score a 10 the second time around. Likewise, I wrote about how someone who scored a 200 the first time would likely score a 190 the second time. You surely remember that example. Well, how is that different from the logic below? There are just not many people who can afford to be unlucky and still score as high as 750. A person scoring 750 was, more likely than not, luckier than average. Since, by definition, luck does not hold from one administration of the test to another, a person scoring 750 on one test is expected to score below 750 on a second test. This does not mean that they necessarily will score less than 750, just that it is likely. The same logic can be applied to someone scoring 250. Since there are more people with "true" scores between 250 and 300 than between 200 and 250, a person scoring 250 is more likely to have a "true" score above 250 and be unlucky than a "true" score below 250 and be lucky. This means that a person scoring 250 would be expected to score higher on the second test. For both the person scoring 750 and the person scoring 250, their expected score on the second test is between the score they received on the first test and the mean. Next time, don't call me a moron unless you can back it up. Link to comment Share on other sites More sharing options...
EC-Bills Posted November 9, 2006 Share Posted November 9, 2006 Where's NJSue when you need her?!? Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 9, 2006 Share Posted November 9, 2006 I suggest you reread the bolded sentence in the quote from my above post.Regression toward the mean occurs any time people are chosen based on observed scores that are determined in part or entirely by chance. The element of chance--that is, of measurement error--causes scores to seem more widely distributed than they really are. Thus, someone who scored a 750 on the math section of the SAT is expected to get a 725 upon retaking that test. 830319[/snapback] CHANCE AND MEASUREMENT ERROR ARE TWO COMPLETELY DIFFERENT THINGS!!! Link to comment Share on other sites More sharing options...
Ramius Posted November 9, 2006 Share Posted November 9, 2006 The element of chance--that is, of measurement error--causes scores to seem more widely distributed than they really are. Thus, someone who scored a 750 on the math section of the SAT is expected to get a 725 upon retaking that test. CHANCE AND MEASUREMENT ERROR ARE TWO COMPLETELY DIFFERENT THINGS!!! 830493[/snapback] after 18 pages, what makes you think that captain (*^*&%^$^#is going to listen now? I'll give him credit tho, he manages to change his arguement and his parameters with every other post. Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 9, 2006 Share Posted November 9, 2006 after 18 pages, what makes you think that captain (*^*&%^$^#is going to listen now? I'll give him credit tho, he manages to change his arguement and his parameters with every other post. 830652[/snapback] Of course he's not going to listen. I'm just amazed at how many different ways he can find to "regress toward the mean" (sic). Link to comment Share on other sites More sharing options...
Orton's Arm Posted November 9, 2006 Share Posted November 9, 2006 CHANCE AND MEASUREMENT ERROR ARE TWO COMPLETELY DIFFERENT THINGS!!! 830493[/snapback] Nice try, but wrong. If you look at the way the article used the word "chance," it involved the possibility of people getting lucky or unlucky when taking the math section of the SAT test. Please don't tell me you're trying to say that the people the article described got lucky or unlucky on the SAT due to "chance" while the people I described got lucky or unlucky on the I.Q. test due to "measurement error," and that "chance" and "measurement error" have nothing in common. You can't possibly be that desperate, can you? Link to comment Share on other sites More sharing options...
Orton's Arm Posted November 9, 2006 Share Posted November 9, 2006 after 18 pages, what makes you think that captain (*^*&%^$^#is going to listen now? I'll give him credit tho, he manages to change his arguement and his parameters with every other post. 830652[/snapback] My argument has remained consistent, as has your inability to comprehend it. Had you understood the relation between measurement error and regression toward the mean in the first place, the last several pages of this discussion would have been unnecessary. As it is, I had to put together a Monte Carlo simulation to demonstrate a statistical concept with which you should already be familiar. Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 10, 2006 Share Posted November 10, 2006 My argument has remained consistent, as has your inability to comprehend it. Consistency is not a positive when you're as dead-nuts wrong as you are. Had you understood the relation between measurement error and regression toward the mean in the first place, the last several pages of this discussion would have been unnecessary. As it is, I had to put together a Monte Carlo simulation to demonstrate a statistical concept with which you should already be familiar. 831169[/snapback] He understands the relation between measurement error and regression toward the mean: THERE IS NONE. You don't understand the math, so you set up the simulation incorrectly, got bad data out of it, interpreted it incorrectly, and then brought out a whole bunch of links that you didn't understand to support your incorrect interpretation of bad data from your incorrect simulation. And the only reason I'm encouraging you is because it's !@#$ing hilarious, how completely deluded about wrong you are. We can't even explain it to you, because you can't even speak the language; you can't even define "mean" properly! Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 10, 2006 Share Posted November 10, 2006 Nice try, but wrong. If you look at the way the article used the word "chance," it involved the possibility of people getting lucky or unlucky when taking the math section of the SAT test. Please don't tell me you're trying to say that the people the article described got lucky or unlucky on the SAT due to "chance" while the people I described got lucky or unlucky on the I.Q. test due to "measurement error," and that "chance" and "measurement error" have nothing in common. You can't possibly be that desperate, can you? 831161[/snapback] "Chance" is a colloquial term for a stastical principle. "Error" is a completely different principle...it's a function of the accuracy of your test, not the statistical distribution of your results. You would know this if you ever took a statistics class... This is what I mean when I say you can't speak the language. So far, you've proven you can't define "heritability", "variance", "chance", "error", "mean", "regression"... you just keep getting funnier and funnier... Link to comment Share on other sites More sharing options...
Chef Jim Posted November 10, 2006 Share Posted November 10, 2006 Ok, $75 to stop. Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 10, 2006 Share Posted November 10, 2006 Ok, $75 to stop. 831222[/snapback] Stop this? This sh-- is high comedy. But of course, I just read a book about mud, so what the !@#$ do I know... Link to comment Share on other sites More sharing options...
EC-Bills Posted November 10, 2006 Share Posted November 10, 2006 Ok, $75 to stop. 831222[/snapback] What are you talking about? I said it before, this thread is the gift that keeps on giving Link to comment Share on other sites More sharing options...
Chef Jim Posted November 10, 2006 Share Posted November 10, 2006 Stop this? This sh-- is high comedy.But of course, I just read a book about mud, so what the !@#$ do I know... 831225[/snapback] Yeah, about as funny as a Chef telling some idiot how long it takes to cook.........oh, carry on. Link to comment Share on other sites More sharing options...
Orton's Arm Posted November 10, 2006 Share Posted November 10, 2006 Consistency is not a positive when you're as dead-nuts wrong as you are.He understands the relation between measurement error and regression toward the mean: THERE IS NONE. You honestly didn't understand a single word of that article, did you? I'm dumbfounded that you and Ramius can claim to understand so much about statistics, yet display so abysmally poor an understanding of regression toward the mean. The two of you have gone on for pages about this, but not once has either of you even so much as hinted at a flicker of understanding about this concept. I feel a deep sense of pity for whichever poor fools attempted to teach either of you stats. I've tried explaining this for pages, and you still don't get it. As though lack of comprehension wasn't bad enough, the two of you actually jeered at what's been a consistent, correct, and clear explanation of regression toward the mean. In my simulation, 1000 people were assigned true I.Q.s Then each person was given an I.Q. test where the result was the person's true I.Q. modified by an error term. Those who did the best on this I.Q. test were assigned to the Threshold group. Threshold members were given a second test; again based on their true I.Q. and the same error formula as before. As a whole, the Threshold group consistently did slightly less well on the second test than on the first. If you look at the link I provided, and explore around a little, you'll find a link to a simuation. In that simulation, people are given a test of some sort, which is based partly on luck and partly on innate ability. Those who score above whichever threshold you choose are retested. They'll do a little less well the second time around. It's the exact same simulation, except that I developed mine before I learned anything at all about theirs. Not only are the simulations conceptually the same, but my explanations (using I.Q. tests where people score 200, 190, 180, etc.) have been conceptually the same as the explanations provided by the website WRT math scores on the SAT. There is not a single error in anything I've written about regression toward the mean. The fact you're still trying to find such an error only exposes your own ignorance about the issue. As you were unable to understand the explanations aimed at adults, I suggest you learn about the topic here. In particular, I want to draw your attention to the following quote: We want to test whether a certain stress-reducing drug increases reading skills of poor readers. Pupils are given a reading test. The lowest 10% scorers are then given the drug, and tested again, with a different test that also measures reading skill. We find that the average reading score of our group has improved significantly. This however does not show anything about the effectiveness of the drug: even without the drug, the principle of regression toward the mean would have predicted the same outcome. Link to comment Share on other sites More sharing options...
meazza Posted November 10, 2006 Share Posted November 10, 2006 Ok, $75 to stop. 831222[/snapback] I'm willing to donate $75 as well. Link to comment Share on other sites More sharing options...
Wacka Posted November 10, 2006 Share Posted November 10, 2006 This crap is still going on? Link to comment Share on other sites More sharing options...
Bungee Jumper Posted November 10, 2006 Share Posted November 10, 2006 If you look at the link I provided, and explore around a little, you'll find a link to a simuation. In that simulation, people are given a test of some sort, which is based partly on luck and partly on innate ability. Those who score above whichever threshold you choose are retested. They'll do a little less well the second time around. It's the exact same simulation, except that I developed mine before I learned anything at all about theirs. And you don't understand the article, because it's not error causing the effect. Error has nothing to do with it; it's a feature of a normally distributed sample. The regression is not because of "error", the regression is because it's a statistical function, not a deterministic one. Which is your main hang-up in this entire discussion. You can't define "statistics". It's not deterministic, it's probabilistic...which is what causes regression, and it's not error either. So we'll just add that to the list of words you can't define: "deterministic", "statistical", and "probability". Hell, let's just say you can't define "math". Link to comment Share on other sites More sharing options...
meazza Posted November 10, 2006 Share Posted November 10, 2006 This crap is still going on? 831284[/snapback] Care to donate to the kill a thread fund. Link to comment Share on other sites More sharing options...
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