Correlation is the relationship between two variables (Schwartz, 2015). Linear correlation can be described as the measure of intensity of association between two random variables (Statlect.com, 2016).
In other words, correlation quantifies the linear association between two variables (The Math Works, 2016). When describing this relationship several factors have to be considered.
Two variables that have a small or no linear correlation can have a strong nonlinear association. Also, when there is no correlation between the two variables, the values of the variables will not have a tendency to decrease or increase in tandem (The Math Works, 2016).
Correlation does not indicate or suggest existence of a statistically significant or casual relationship between conduct and result. There are set of principles of casual interference that need to be satisfied to imply cause and effect (Schwartz, 2015).
Here the dose-response effect has to be considered to establish the cause-and effect, i.e., the value of response variable has to change in a meaningful way with the dose or level of the suspected casual agent.
Also, the hypothesis that there is a casual relationship between number of cigarettes and pulse rate is not consistent with the current biological or theoretical knowledge.
In the given conclusion the error is that the pulse rate increases as the number of cigarettes smoke increases does not meet the dose-response criteria, is inconsistent finding, biologically incredible, and has no coherence of evidence.
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