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Correlation and Causation are amongst the core concepts of statistical analysis. It is an analytical method of defining relationships between two or more variables. Yet, students often confuse the two when it comes to assessing the relationship of the said variables. So then they look for correlation and causation coursework help online. And yes, you lucked out when you landed on this page because we could help you clear some concepts on the topic.
Economics is a fundamental part of our daily lives, and we come across ample practical situations to understand the theoretical concepts of our textbooks. Let’s understand this statistical topic of correlation and causation with an example.
Let’s hypothesise that in a survey conducted by an expert, it was found that the sales of Pot Noodle and a bottle of Coke went up simultaneously in a defined area and setting. But does this necessarily mean the sales of pot noodles caused an increase in sales of Coke? This shall be answered best by understanding the concept of correlation and causation.
Correlation is the fact of two variables being associated with each other so that if one of them changes, there is some degree of change observed in the other. It basically defines the relationship and the degree of interrelation between them. Correlation may be linear, non-linear, positive, negative, or sometimes not related at all.
Causation means that one event or variable causes a direct impact on the other. Causation is determined by grouping together independent events or variables, subjecting them to identical conditions, and then analyzing the differences in the outcomes. If only a significant difference is observed, it can be said that there is a causation principle underlying the experimental setup.
Coming back to our example, we can safely conclude that Pot Noodle and Coke sales are correlated but not causative of each other.
As explained eloquently by our Correlation and Causation dissertation experts, it is established that the two are entirely different concepts. Let’s also know the reasons that indicate correlation does not mean causation.
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