What is leverage in statistics?

In statistics, leverage is a measure of how much an observation influences the prediction made by a statistical model. It is also commonly referred to as influential points or outliers.

Leverage is calculated using a formula that takes into account the distance between a particular data point and the mean of all the other data points, as well as the spread of the data.

High leverage points can have a large impact on the estimates of a regression model. They can either increase or decrease the slope of the line of best fit, depending on whether they are above or below the mean of the other data points.

To identify leverage points, statisticians will typically use a diagnostic tool called a scatterplot, which plots the data points in a two-dimensional space. Points that are far away from the rest of the data points are likely to be influential, and thus, should be investigated further.

Overall, understanding leverage is important in statistics because it can help analysts identify unusual observations that may be driving the results of their models.