The term spurious is derived from the Latin origin. It is meant to mean not true or false. Management and spurious correlations are terms that are used in the field of statistics to make assumptions about certain things and calculations. When looking at the term spurious relationship, it can duly be noted that it is commonly used in statistics and in a particular way to provide certain answers. It is specifically used in particular to help in the experimental research techniques which would go a long way in answering some questions. Using spurious correlation in experimenting in research techniques help in the understanding and prediction of other relationships, namely direct causal relationship. An example of a direct causal relationship can be (X > Y). There is also a relationship called a non-causal correlation. A non-causal correlation may and can be created spuriously by a factor that is called an antecedent. An antecedent, for example, causes both the relationship as shown; (W > X and W > Y). Certain variables, known as intervening variables if undetected may make the indirect causations seem to look direct (Wooldridge, 2009). An example of an intervening variable can be seen in the form of (X > W > Y). Because of the effect of indirect causations being made to look direct, correlations that have been identified through experiment are not seen to represent relationships otherwise known as casual. This is unless spurious relationships can be ruled out. Only then can it happen. The purpose of this essay is to bring to light the importance of management and spurious correlations as used in statistics. As the name suggests, the essence of this essay is to bring out the importance of identifying correlations before they become misleading. This topic is important because many people tend to confuse the relationship between variables and by the end of this essay, it will be clear on the methods. In order to become a statistician or a social scientist, it is paramount that one understands that correlation be shown by being proven statistically. The purpose of this paper is to illustrate the widespread occurrence of spurious correlation (Kleinbaum & Kleinbaum, 2008). In order to test and prove whether a correlation between two variables or constants is genuine or spurious, there are some additional variables and equations that have to be introduced in order to widen the parameters of getting legit results. There have to be sufficient assumptions being made and they must be made in order to help with the proper identification. This proper identification is of the parameters of a wider system and will help in the obtaining of results. It the results are found that the two variables, which were original, are causally related in a wider system, then the conclusion of the correlation is that it is genuine. The difference between a true and spurious correlation is that a true correlation exists and does not have to be proven while a spurious one needs to undergo experiments in order to be proven to be wrong. Statistical research has been affected by the identification problem, where many statisticians have been unable to conclude on where the problem is. An example of spurious corr
Management and Spurious Correlation Name Professor Course Date Introduction Management and spurious correlation can be described as a mathematical relationship whereby there are two events or variables that have no direct or casual connection with each other but still may be identified as two events or variables that have a connection due to a wrong identification or due to coincidence…
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6 pages (1500 words)Research Paper
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