If a causal link needs to be established, then further analysis to control or account for other potential variables effects needs to be performed, in order to rule out other possible explanations. Strong correlation means that there arent many outliers. Problem 1: Flower height and petal length. It is possible that the observed relationship is driven by some third variable that affects both of the plotted variables, that the causal link is reversed, or that the pattern is simply coincidental.įor example, it would be wrong to look at city statistics for the amount of green space they have and the number of crimes committed and conclude that one causes the other, this can ignore the fact that larger cities with more people will tend to have more of both, and that they are simply correlated through that and other factors. There is a strong, positive, linear association between the two variables. Scatterplots are really good for helping us see if two variables have positive or negative association (or no association at all). A line can have positive, negative, zero (horizontal), or undefined (vertical) slope. Slope is a measure of the steepness of a line. Note that weak/strong does not indicate whether the linear relationship. A scatter plot is a plot of the dependent variable versus the independent variable and is used to investigate whether or not there is a relationship or connection between 2 sets of data. This gives rise to the common phrase in statistics that correlation does not imply causation. On the other hand, if there are a lot of large gaps, the correlation is said to be weak. Simply because we observe a relationship between two variables in a scatter plot, it does not mean that changes in one variable are responsible for changes in the other. This is not so much an issue with creating a scatter plot as it is an issue with its interpretation.
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