Linear correlation is a measure of dependence between two random variables. A linear correlation tells the strength of the relationship between the two variables. The results of the linear correlation analysis may be a positive correlation, a negative correlation, or no correlation. The conclusion of cigarettes cause the pulse rate to increase is not valid. The linear correlation does not give the results of the relationship between the two variables. Correlation does not imply causation in this sample. In a causal relationship the variables have an effect on each other and increases when one another do. In this study the only variables shown are smoking and heart rate. It shows no other variables to
consider such as gender, age, any health factors, or brands of cigarettes. Health factors along with other considerations may cause an increase in the pulse rate by itself. This sample does not give any indications of linear correlation between the two variables. There is no evidence in the statement “cigarettes cause the pulse rate to increase” to make it true without more evidence of both variables. Further research must be performed for the statement to valid. The research should include age, gender, health factors, and brands of cigarettes.
It seems like that the conclusion “Cigarettes cause the pulse rate to increase” is just not taking into consideration other variables and the heart rate can only increase so much. At that point, it would not matter how many more cigarettes a person smoked, the heart rate would not increase at the same rate. The linear correlation would change as the persons heart rate increases and the more cigarettes they smoked. The change in heart rate between the first and second cigarette will not be the same as the change in heart rate between the ninth and tenth cigarette. Identifying the associations between variables is the goal for correlational analyses. If the hypothesis is “Is there a relationship between the number of cigarettes smoked and pulse rate?”, then there is more room to talk about the relationship and arrive at a conclusion that yes, there is a relationship between the variables whether it is positive or negative. A better conclusion might be: There was a statistically significant correlation between the number of cigarettes smoked and pulse rate. This statement also needs to be backed up with results from a test like the Pearson product-moment correlation coefficient. Then the conclusion would be statistically relevant (Grove & Cipher, 2017).