![]() ![]() 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. This produces this cone shape that you can see on your plot. 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. This means that as variable 3 increases its value, the variance of variable 1 increases. Cross-reference overlapping question: 'Correct or incorrect interpretation of scatter plots: a comparison among the Pearson, Spearman and Kendall correlations'. But additionally, these two variables seem to have what is called heteroscedasticity. 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. The scatter plot has been called the most useful invention in the. Load the Gapminder data, drop Oceania, load packages. This includes mean, covariance and correlation. We'll focus on correlation, which is a measure of how two variables. R, containing no spaces or other funny stuff, and evoking scatter plots and lattice. ![]() 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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