A residual plot is primarily used to evaluate what aspect of regression analysis?

A residual plot is primarily used to evaluate model assumptions in regression analysis. When conducting regression, one of the key assumptions is that the residuals (the differences between observed and predicted values) should exhibit certain characteristics, indicating that the model is appropriate for the data.

By examining a residual plot, you can assess whether these assumptions hold. For instance, the residuals should be randomly scattered around zero, reflecting a good fit of the model and ensuring that the errors are independent and identically distributed. Patterns in the residuals, such as non-random distributions or trends, may suggest that the model is not capturing all the necessary information, pointing to issues such as non-linearity or heteroscedasticity.

In contrast, correlation strength relates to how well two variables are related, data variability concerns the spread of data points, and sample size pertains to the number of observations in the dataset. While these factors are important in regression analysis, they do not specifically indicate the validity of the model assumptions, which is the primary purpose of a residual plot.