In essence, finding a weak correlation that is statistically significant suggests that that particular exposure has an impact on the outcome variable, but that there are other important determinants as well. If that is the case, even a weak correlation might have be statistically significant if the sample size is sufficiently large. As a result, height might be a significant determinant, i.e., it might be significantly associated with BMI but only be a partial factor. So, height is just one determinant and is a contributing factor, but not the only determinant of BMI. For example, body mass index (BMI) is determined by multiple factors ("exposures"), such as age, height, sex, calorie consumption, exercise, genetic factors, etc. How can a correlation be weak, but still statistically significant? Consider that most outcomes have multiple determinants. There is quite a bit of scatter, but there are many observations, and there is a clear linear trend. It suggests a weak (r=0.36), but statistically significant (p<0.0001) positive association between age and systolic blood pressure. The scatter plot below illustrates the relationship between systolic blood pressure and age in a large number of subjects. The four images below give an idea of how some correlation coefficients might look on a scatter plot. Also, keep in mind that even weak correlations can be statistically significant, as you will learn shortly.
#Negative and linear scatter plot how to
The table below provides some guidelines for how to describe the strength of correlation coefficients, but these are just guidelines for description. 0.2917043 Describing Correlation Coefficients For example, we could use the following command to compute the correlation coefficient for AGE and TOTCHOL in a subset of the Framingham Heart Study as follows: Instead, we will use R to calculate correlation coefficients. You don't have to memorize or use these equations for hand calculations. Where Cov(X,Y) is the covariance, i.e., how far each observed (X,Y) pair is from the mean of X and the mean of Y, simultaneously, and and s x 2 and s y 2 are the sample variances for X and Y.
Nevertheless, the equations give a sense of how "r" is computed. We will use R to do these calculations for us. However, you do not need to remember these equations. The equations below show the calculations sed to compute "r". The scatter plot suggests that measurement of IQ do not change with increasing age, i.e., there is no evidence that IQ is associated with age.Ĭalculation of the Correlation Coefficient Possible values of the correlation coefficient range from -1 to +1, with -1 indicating a perfectly linear negative, i.e., inverse, correlation (sloping downward) and +1 indicating a perfectly linear positive correlation (sloping upward).Ī correlation coefficient close to 0 suggests little, if any, correlation. The sample correlation coefficient (r) is a measure of the closeness of association of the points in a scatter plot to a linear regression line based on those points, as in the example above for accumulated saving over time.
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