![]() ![]() In books/guides about scatter plots interpretations, I am not able to find anything like the plots (1,2) and (1,3) (or equivalently the plots (2,1) and (3,1)), where the correlation is. In the following plot I remark in red the outliers, and in green an estimation of how the regression line would be if you removed this outlier. Cross-reference overlapping question: 'Correct or incorrect interpretation of scatter plots: a comparison among the Pearson, Spearman and Kendall correlations'. ![]() This produces this cone shape that you can see on your plot. This means that as variable 3 increases its value, the variance of variable 1 increases. For instance, the relationship between height and weight have a positive correlation. Positive and Negative Correlation and Relationships Values tending to rise together indicate a positive correlation. But additionally, these two variables seem to have what is called heteroscedasticity. Scatterplots display the direction, strength, and linearity of the relationship between two variables. In here, in the bottom right corner you can see again the outlier that is also affecting the correlation and the regression line between these two variables. So, if you removed this datapoint, the correlation will likely increase, and the regression line between this two variables will move upwards, fitting better your data. This includes mean, covariance and correlation. A point that differs significantly from other observations, like this one, is called an outlier, and it greatly affect the computations that are based on the mean. In this plot, in the bottom right corner you can see a data point that is behaving pretty strange compared to the rest of your datapoints. ![]()
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