Thus y=birth weight and x=gestational age. In this example, birth weight is the dependent variable and gestational age is the independent variable. We wish to estimate the association between gestational age and infant birth weight. Scenario 4 might depict the strong negative association (r= -0.9) generally observed between the number of hours of aerobic exercise per week and percent body fat.Įxample - Correlation of Gestational Age and Birth WeightĪ small study is conducted involving 17 infants to investigate the association between gestational age at birth, measured in weeks, and birth weight, measured in grams.Scenario 3 might depict the lack of association (r approximately = 0) between the extent of media exposure in adolescence and age at which adolescents initiate sexual activity.Scenario 2 depicts a weaker association (r=0,2) that we might expect to see between age and body mass index (which tends to increase with age).Scenario 1 depicts a strong positive association (r=0.9), similar to what we might see for the correlation between infant birth weight and birth length.The figure below shows four hypothetical scenarios in which one continuous variable is plotted along the X-axis and the other along the Y-axis. Graphical displays are particularly useful to explore associations between variables. Therefore, it is always important to evaluate the data carefully before computing a correlation coefficient. It is important to note that there may be a non-linear association between two continuous variables, but computation of a correlation coefficient does not detect this. A correlation close to zero suggests no linear association between two continuous variables. The magnitude of the correlation coefficient indicates the strength of the association.įor example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association. The sign of the correlation coefficient indicates the direction of the association. The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other). Ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two variables. The sample correlation coefficient, denoted r, In correlation analysis, we estimate a sample correlation coefficient, more specifically the Pearson Product Moment correlation coefficient.
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