Chance News 16: Difference between revisions
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<blockquote>"After the flood the earth was completely different than the earth before. There were widespread global differences. These would include changes in the climate, composition of the atmosphere, hydrologic cycle, geologic features, cosmic radiation reaching the earth, ozone concentration, ultra violet light, background radiation, genetics, diet, and a host of other subtle and/or profound chemical and physiological changes. These changes caused a rapid decline of the longevity of post flood humanity."</blockquote> | <blockquote>"After the flood the earth was completely different than the earth before. There were widespread global differences. These would include changes in the climate, composition of the atmosphere, hydrologic cycle, geologic features, cosmic radiation reaching the earth, ozone concentration, ultra violet light, background radiation, genetics, diet, and a host of other subtle and/or profound chemical and physiological changes. These changes caused a rapid decline of the longevity of post flood humanity."</blockquote> | ||
The statistical model comes in the analysis of the decline in ages after the flood. An exponential decay model produces the following equation y=487.78exp(-0.0907x) where x represents the generation number. After 20 generations, the ages | The statistical model comes in the analysis of the decline in ages after the flood. An exponential decay model produces the following equation y=487.78exp(-0.0907x) where x represents the generation number. After 20 generations, the ages settles down to a more modern figure of 70 years. The coefficeint of determination is 0.889. | ||
<blockquote>This means that the decay rate of the patriarch's death after the flood was only 11% from being a perfect match.</blockquote> | <blockquote>This means that the decay rate of the patriarch's death after the flood was only 11% from being a perfect match.</blockquote> | ||
Revision as of 22:45, 4 April 2006
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Exponential decay in Biblical ages
WHY did people live longer BEFORE Noah's Flood than they did after it? written by Arnold C. Mendez, Sr. and published at BibleStudy.org.
While looking on the web for good examples of the coefficient of determination, I came across the a statistical analysis of the ages of Biblical patriachs. Apparently the author believes in a literal interpretation of the Bible and wants to answer skeptical comments about the unusual ages reported for some of the patriarchs in the Bible.
"One of the most intriguing facts in the Bible is the immense life spans of the patriarchs before and just after the flood. Adam lived 930 years, Methuselah the longest lived of the patriarchs lived 969 years. Noah lived 950 years."
Why is it that no one today lives so long?
"After the flood the earth was completely different than the earth before. There were widespread global differences. These would include changes in the climate, composition of the atmosphere, hydrologic cycle, geologic features, cosmic radiation reaching the earth, ozone concentration, ultra violet light, background radiation, genetics, diet, and a host of other subtle and/or profound chemical and physiological changes. These changes caused a rapid decline of the longevity of post flood humanity."
The statistical model comes in the analysis of the decline in ages after the flood. An exponential decay model produces the following equation y=487.78exp(-0.0907x) where x represents the generation number. After 20 generations, the ages settles down to a more modern figure of 70 years. The coefficeint of determination is 0.889.
This means that the decay rate of the patriarch's death after the flood was only 11% from being a perfect match.
You can view a graph of the data and the fitted line below, taken directly from the article.
http://www.biblestudy.org/basicart/longflod.gif
The author argues that the ages must be genuine since they fit an exponential curve so well and the writers of the time would not have the mathematical sophistication to fake an exponential decay curve.
Questions
1. Does a coefficient of determination of 89% sound impressive to you? What do you think about the author's comment that this is "only 11% from being a perfect match"?
2. What other models (linear or non-linear) would be worth considering for this data?
3. Is there an alternative explanation for this pattern of ages?
Submitted by Steve Simon
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