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Linear correlation refers to a straight-line relationship between two variables. Linear correlation analysis can determine if there is a relationship between variables, either a positive or a negative correlation.  A positive correlation means that as one variable increases or decreases the other variable increases or decreases whereas a negative correlation is when one variable increases the other decreases. In the example provided, it would be considered a positive correlation because cigarette smoking increases pulse rate. Correlation does not necessarily prove causation. Causality is a way to know if one event causes another (Grove, Gray, and Burns, 2015).  In order to test for correlation you can conduct a t-test if between 2 variables or a chi test, ANOVA, or ANCOVA for three or more variables. The conclusion indicates that cigarette smoking causes the pulse rate to increase but performing a test as mentioned above would allow for the conclusion to be stated as other variables such as age, amount of cigarettes, coronary artery disease can all be variables that increase pulse rate.


Grove, S., Gray, J., & Burns, N. (2015). Understanding statistics in research. In Understanding nursing research (6th ed., pp. 340-348). Saint Louis, MO: Elsevier Saunders.

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