标题: 如果快速区分两个现象或者事件之间的关系是cause-effect还是correlation [打印本页] 作者: angelcity 时间: 2011-6-24 16:48 标题: 如果快速区分两个现象或者事件之间的关系是cause-effect还是correlation An industry analyst asserted in his recent report that the relative scarcity of housing in a particular market leads to larger than normal increases in price. During the late 1990s, according to the analyst's report, occupancy rates-a measure of the percentage of housing occupied at a given time-in crowded urban markets such as New York and San Francisco hovered around 99.5%. During the same period, homing prices increased by as much as 100% per year, compared to more riormal past increases in the range of 5% to 15% per year. Which of the following is an assumption thatsupports the analyst's assertion? (A) In the housing market, there generally must be at least five buyers per seller in order to cause larger than normal increases in price. (B) Increases in demand often reflect an influx of new buyers into the marketplace or an unusual increase in buying power on the part of the customer. (C) The u.s. housing market showed a larger than average increase in the 1990s across the country, not just in crowded urban areas. (D) Price increases do not cause people to withhold their houses from the market in the hopes that prices will increase even further in the future. (E) A significant rise in housing prices in a specific area may cause some potential buyers to relocate to other, less pricey areas.
The correct answer choiceeliminates an ALTERNATE MODEL OF CAUSATION. The analyst claims that, in the housing market, scarcity causes larger than normal price increases. The remaining premises are all facts that do not appear to have any logic gaps or other problems. The premises, however, represent only a correlation, not a cause-effect relationship: during a certain time period, occupancy rates were high and prices increased a great deal. The analyst claims that scarcity causes the price increase, but the reverse could also be true: the price increase could cause the scarcity. Perhaps people wait to sell because they think the market will continue to rise, or perhaps people will not sell their own property because they would then have to pay inflated prices for a new property. In order to conclude that scarcity causes prices to rise, the analyst must assume that the "reverse" causation does not occur. 选E. 今天看到manhattan上这题.分析里说到这句represent only a correlation, not a cause-effect relationship,我就想问如何在做题的时候比较快速清楚地区分是cause-effect,还是correlation?区分它们对判断选项有什么影响? 谢谢!作者: angelcity 时间: 2011-6-24 17:24
\Assumptions can diminate alternate causes for a given conclusion. Many GMAT conclusions are statements of cause and effect. For some of these cause-andeffect conclusions, the given premises simply note a correlation between two phenomena, without commenting on any causal relationships between them. A correlation means that two things occur together, without necessarily indicating why. For example, a premise might say this: Scientists have discovered that people with Elmer's disease have elevated levels of elastomer in their blood. What do we know? Elmer's disease and elastomer show up together. In other words, these two phenomena are correlated. But we do not know whether elevated levels of elastomer causes Elmer's disease, or whether Elmer's disease causes elevated levels of elastomer, There is even a third possibility: both Elmer's disease and elevated levels of elastomer could be caused by some third, unknown factor (perhaps a genetic defect). In short, correlation is not the same as causation. 后来又找到这样一些说明。好象明白了为什么要去区分cause-effect和correlation.是因为cause-effect已经固定了两个事件发生的走向,那就没有必要再去考虑选项是否存在Alternate model of causation的问题。但如果只是correlation的关系，那就有必要去考虑Alternate model of causation的问题，因为两个现象谁都有可能成为原因。 但是有什么办法比较快速地区分cause 还是correlation么？作者: sdcar2010 时间: 2011-6-24 21:26
correlation: two events happen at the same time.
causation: one event causes another event.
Until proven otherwise, two events which happen at the same time or in tandem are just correlated events.作者: angelcity 时间: 2011-6-25 12:29
thanks a million!