Can some one please help me 6. Based on the forecast error graphed over time sho
ID: 387630 • Letter: C
Question
Can some one please help me 6. Based on the forecast error graphed over time shown below and what we have discussed about unbiasedness, which statement(s) below are correct? -3 -10 9 11 13 5 7 Time 6a. (4 points) From the graph, a bias can be observed from time index 11 to 17 6b. (4 points) A new model needs to be developed starting from time index 11 to represent the change in the trend. 7. (4 points) Survivorship bias involves that selective data which excludes critical information can lead to flawed decision-makingExplanation / Answer
6. FORECAST BIAS
A common property of any good forecast is that it is not biased. Whenever there are differences between actual outcomes and previously generated forecasts of those quantities that is, the forecasts have a general tendency to be very high (over-forecast) or very low (under-forecast), it is said to have a forecast bias.
As per Rick Glover, Bias can be calculated as follows:
‘BIAS = Historical Forecast Units (Two months frozen) minus Actual Demand Units’
In case if forecast bias is encountered, correction of the forecast error is achieved by adjusting the incorrect forecast by an appropriate amount in the appropriate direction:
Thus, following are the answers to the above questions:
6a. TRUE; since from time 11 it can be seen that the forecast error is quite different from the historical forecast units as per Rick Glover’s formula
6b. FALSE; As stated above, the forecast bias can be dealt with via appropriate correction in appropriate direction. Thus, no model needs to be developed.
7. SURVIVORSHIP BIAS
Survivorship bias is a type of selection bias in which people tend to focus on some people/thing as a part of a selection process that made through the process while overlooking those which did not make through. This bias often leads to false conclusions and overtly biased decisions.
Thus, the answer is as follows:
TRUE; Survivorship Bias involves selective data which excludes critical information can lead to flawed-decision making