![]() ![]() Essentially, correlation is the measure of how two or more variables are related to one another. Scatterplots typically contain the following elements: X-axis representing values of a continuous variable. I graphed these two variables in a scatterplot to assess the relationship between them. However, when used in a technical sense, correlation refers to any of several specific types of mathematical operations between the tested variables and their respective expected values. Example Scatterplot During an experiment, I measured the Body Mass Index (BMI) and body fat percentage of adolescent girls. In informal parlance, correlation is synonymous with dependence. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).įormally, random variables are dependent if they do not satisfy a mathematical property of probabilistic independence. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related.įamiliar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.Ĭorrelations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, if a company creates a self-driving car and the correlation between the car’s turning decisions and the probability of avoiding a wreck is r 0. In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. For example, a correlation of 0.9 indicates a very strong positive correlation a change in a first variable is a strong indicator of a similar change in a second variable. In technology fields, the correlation between variables might need to be much higher to even be considered weak. N.B.: the figure in the center has a slope of 0 but in that case, the correlation coefficient is undefined because the variance of Y is zero. The correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). Several sets of ( x, y) points, with the Pearson correlation coefficient of x and y for each set. ![]()
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