Correlation refers to the relationship between the two variables. The positive correlation means that the increase in one variable causes the increase in the other; the negative correlation is indicated when the increase in one causes the decrease of the other variable. Identification of correlation is based on the experimental data, namely the different levels of two variables are studied and contrasted using mathematical tool (correlation coefficient) or the graph.
The research on the relation between the number of cigarettes smoked a day and the pulse rate indicated the positive linear correlation. Therefore, the more cigarettes a person smokes, the higher is the pulse rate. The conclusion states that cigarettes cause the pulse rate to increase. However, there is a limitation in this study, since the pulse rate cannot increase to the unlimited values. On the contrary, the number of cigarettes smoked a day is more flexible. This remark shows that the relation is not direct, and correlation does not mean causation.
The linearity of correlation is characterized by determination coefficient (R2), which indicates how well the data fit the line. The closer R2 is to 1, the better the line explains the relation between the variables. However, for the applied research, R2<<1, and the value indicates which part of the deviation in variable 1 is explained by variable 2. For example, R2 = 0.64 means that 64% of the pulse rate change is explained by the number of cigarettes, and the rest 36% are attributed to other factors (Vogt & Johnson, 2012).
There are certain errors associated with this research. People may not remember the exact number of cigarettes smoked. There are factors that influenced the pulse rate during the study, namely physical activity, or naturally high pulse rate. Thus, the conclusion has to be formulated as: the increase in number of cigarettes smoked has impact on increase of the pulse rate.
References
Vogt, W. P., & Johnson, B. (2012). Correlation and regression analysis. Los Angeles, Calif: SAGE.
