Correlation in statistics refers to the association or dependence of two variables. The relationship can be casual or fails to be causal between the bivariate data or the random variables. Correlations are different based on the broad statistical class relationship demonstrating dependence. However, commonly, the use of correlation prevails when two close variables contain a linear relationship. Correlations help in predicting the relationship that can be explored in a real-world situation. A perfect example is the increased pulse rates that result from increased consumption of cigarettes. The causal relationship is present in the example as increased smoking increases the pulse rate of smokers. However, it is critical to understand that correlation availability is not enough to conclude a causal relationship (Elouerkhaoui, 2017).
Linear correlations analysis is a tool that helps discern if some two sets of variables are related. Hence, there are three possible outcomes of the correlation, which might be a positive, negative, or no correlation. Causations are attained from correlation. This implies that correlation has no implication on causation. The discussion question above is considered problematic because most models are consistent with all the correlation (Elouerkhaoui, 2017). This means that two variables may attain their correlations from dozen other variables, including the third and the fourth ones excluding the two main variables under comparison. The conclusion error is as a result of Association as causation.
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The mistaken assumption infers that when two events occur simultaneously, one is considered as the reason for the second one. One might argue that cigarette consumption positively correlates to the increase in pulse rate. However, there is no evidence that demonstrates that consumption of cigarette results in an increase in the pulse (Elouerkhaoui, 2017).
References
Elouerkhaoui, Y. (2017). Credit correlation: Theory and practice .
