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The Jets Rule: If the New York Jets win their second away game, the Republican wins the White House. Otherwise, the Democrat wins.
I got home today in time to catch the final 6:00 minutes of the 4th quarter. NY and Miami tied 20-20. Game goes into overtime. NY wins the coin toss - heads. Jets fail to score a touchdown, ball goes to Miami, incredible pass .... and then the Dolphins try for a FG. I sat here putting the whammy on Miami - miss, miss, miss .... and miss they did ...... Jets take back the ball, run it down to the 18 yard line ..... FG ........ and it's GOOD! Game over, Jets win 23-20!
I got home today in time to catch the final 6:00 minutes of the 4th quarter. NY and Miami tied 20-20. Game goes into overtime. NY wins the coin toss - heads. Jets fail to score a touchdown, ball goes to Miami, incredible pass .... and then the Dolphins try for a FG. I sat here putting the whammy on Miami - miss, miss, miss .... and miss they did ...... Jets take back the ball, run it down to the 18 yard line ..... FG ........ and it's GOOD! Game over, Jets win 23-20!
Bye bye Bammy!
It was a great game. Our own Shawn Greene continues to do well. I do, however, have my doubts about the statistical "significance" of these coincident events.
One simply needs to understand that there are random events which can occur at the same time, giving the impression of a "correlation". The uninitiated look upon this as a "cause and effect" phenonomenon, but it is far from the truth.
Here is the problem-
When one is analyzing the influence of ONE VARIABLE on the outcome of ONE EVENT, one can use a statistical "t" test as a measure of statistical significance. However, when analyzing MULTIPLE VARAIBLES and ONE OUTCOME, a statistical error occurs when using a single "t" test, as the probability that this event occurred due to chance increases with the number of variables evaluated.
Have no fear! Statistics to the rescue. One can use an analysis of variance (ANOVA) with a Neuman-Keuls correction for multiple variables to determine the true significance of a particular variable on a measured result. This eliminates the possibility of chance leading to a spurious result.
Enjoy football, as it is fun, but it does not predict other events.
Will be looking for the Hawks to get 17 first downs when they play SF on 10/18.
They can lose - just so long as Mitt wins!
Quote:
The Seahawks Rule: If Seattle gets at least 18 first downs in its third-to-last game before the election, the incumbent party wins. Otherwise, the out-of-power party wins.9/9This year: at San Francisco on Oct. 18
It was a great game. Our own Shawn Greene continues to do well. I do, however, have my doubts about the statistical "significance" of these coincident events.
One simply needs to understand that there are random events which can occur at the same time, giving the impression of a "correlation". The uninitiated look upon this as a "cause and effect" phenonomenon, but it is far from the truth.
Here is the problem-
When one is analyzing the influence of ONE VARIABLE on the outcome of ONE EVENT, one can use a statistical "t" test as a measure of statistical significance. However, when analyzing MULTIPLE VARAIBLES and ONE OUTCOME, a statistical error occurs when using a single "t" test, as the probability that this event occurred due to chance increases with the number of variables evaluated.
Have no fear! Statistics to the rescue. One can use an analysis of variance (ANOVA) with a Neuman-Keuls correction for multiple variables to determine the true significance of a particular variable on a measured result. This eliminates the possibility of chance leading to a spurious result.
Enjoy football, as it is fun, but it does not predict other events.
I really don't care for football much at all, I only watched and rooted because I had read of that rule the Jets Rule the other day. Never underestimate the power of my whammies.
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