Linear correlation analysis is used in statistics in the measurement of relationship, causation, significant of co-variation between at least two variables or more, and degree of significance

Linear correlation analysis is used in statistics in the measurement of relationship, causation, significant of co-variation between at least two variables or more, and degree of significance. By stating in conclusion that cigarettes cause the pulse rate to increase through this data is jumping into conclusion as we can only imply from this information that there is a relationship only. However, relationship does not mean causation. Because one factor (the number of cigarettes smoked) seems to cause the pulse rate to increase, it does not say the casual relationship between two variables. Linear correlation looks at the strength of relationships between two or more variables, but does not look at their casual relationships (Tay, 2014). Think of it as a see-saw. When one side gets sat on, the other side goes up. However, we cannot say through linear correlation analysis regarding the casual relationship between the two sides. Suppose the person on one other side is twice the weight of the person on the other end. In order for the other end of the see-saw to go up, it requires the heavier person to kick with their legs from the ground to lift up. That causal relationship factor is not seen through a linear correlation analysis.
In order to prove the casual relationship between the number of cigarettes smoked to the increase of heart rate, further studies need to be performed. More data is needed and a stricter data-collection is also needed. Factors such as activities performed before smoking and before taking the pulse rate, person’s normal heart rate, daily activities performed, if they exercise, medications taken, and more, should also be observed and carefully considered as those can all increase or decrease people’s pulse rate.
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