Adjusting for non-constant errors
In this next example, it appears as though the variance of the response variable increases as the explanatory variable increases. Note that the fix in this exercise has the effect of changing both the variability as well as modifying the linearity of the relationship.
This exercise is part of the course
Inference for Linear Regression in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Run this to see how the model looks
ggplot(hypdata_nonequalvar, aes(x = explanatory, y = response)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
# Model response vs. explanatory
model <- ___
# Extract observation-level information
modeled_observations <- ___
# See the result
modeled_observations
# Using modeled_observations, plot residuals vs. fitted values
___