Linear regression line with an addition of standard error channel projection.

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

Two things to pay attention to:

• The color of the linear regression line is not based on the slope of the linear regression value but on the slope of the corresponding linear regression value for the given point.
• The channel is not a equal linear regression channel : the channel is calculated using standard error and can not be compared to the linear regression channel (except for the middle line).