Answer:
Method 1 has bigger forecast bias.
Step-by-step explanation:
We are given the following information in the question:
Actuals:
23, 10, 15, 19
Method 1 prediction:
23, 5, 14, 20
Residuals = Predicted - Actuals
Sum of square of errors:
[tex]=(23-23)^2+(5-10)^2+(14-15)^2+(20-19)^2\\= 27[/tex]
Mean squared error:
[tex]\dfrac{\text{Sum of squares}}{n} = \dfrac{27}{4} = 6.75[/tex]
Method 2 prediction:
20, 13, 14, 20
Residuals = Predicted - Actuals
Sum of square of errors:
[tex]=(20-23)^2+(13-10)^2+(14-15)^2+(20-19)^2\\= 20[/tex]
Mean squared error:
[tex]\dfrac{\text{Sum of squares}}{n} = \dfrac{20}{4} = 5[/tex]
Since, mean square error of method 2 is less as compared to method 1, method 1 has bigger forecast bias.