2023 Linear correlation analysis is used in statistics in the measurement of relationship causation significant

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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