The difference between the observed value of the dependent variable and the predicted value is called residual.
When you roll a die, you shouldn't be able to predict which number will show on any given toss.
However, you can assess a series of tosses to determine whether the displayed numbers follow a random pattern.
E. g. If the number six shows up more frequently than randomness dictates, you know something is wrong with your understanding (mental model) of how the die actually behaves.
The same principle applies to regression models. You shouldn't be able to predict the error for any given observation.
Residual Plots
Assessing regression assumptions
Residual Plots
How the residuals should look
Question 01
Based on the Residual Plot, which assumption(s) is(are) not met?
- Constant Variance.
- Linearity.
- Both.
- None.
Do you see a funnel shape? Are the points randomly distributed above and below the zero line?
Question 02
Based on the Residual Plot, which assumption(s) is(are) not met?
- Constant Variance.
- None.
- Both.
- Linearity.
Do you see a funnel shape? Are the points randomly distributed above and below the zero line?
Points are not randomly distributed above and below the zero line.
Question 03
Based on the Residual Plot, which assumption(s) are met?
- None.
- Both.
- Linearity.
- Constant Variance.
Do you see a funnel shape? Are the points randomly distributed above and below the zero line?
No clear pattern or funnel shape.
Question 04
Based on the Residual Plot, which assumption(s) are met?
- None.
- Both.
- Linearity.
- Constant Variance.
Do you see a funnel shape? Are the points randomly distributed above and below the zero line?
There is a funnel shape and points are not randomly distributed above and below the zero line.
Question 05
Based on the Residual Plot, which assumption(s) is(are) not met?
- Constant Variance.
- Linearity.
- Both.
- None.
Do you see a funnel shape? Are the points randomly distributed above and below the zero line?
