Final Answer:
A Residuals vs Leverage graph is a visual representation used in regression analysis to identify influential data points and assess the impact of each observation on the model. It helps to detect outliers and influential points by examining the residuals and leverage of each data point.
Step-by-step explanation:
Residuals represent the differences between the observed values and the predicted values from a regression model. The Residuals vs Leverage graph combines information on both residuals and leverage, where residuals are plotted on the vertical axis, and leverage values are on the horizontal axis. Leverage quantifies how much an observation affects the model, and the graph allows for the identification of data points with high leverage and large residuals.
In the graph, data points located in the upper or lower extremes indicate higher leverage, meaning they have a greater influence on the model. Points with large residuals away from the centerline are potential outliers. The combination of these two aspects in the Residuals vs Leverage graph is useful for detecting observations that disproportionately impact the regression model.
This graphical tool is essential in regression diagnostics, helping analysts understand the robustness of their models and make informed decisions about whether specific data points should be included or excluded. By examining the patterns in the Residuals vs Leverage graph, analysts can enhance the reliability and accuracy of their regression analyses.