Final answer:
To calculate the bias of the naïve forecast method, subtract the actual observed values from the forecasted values, sum these differences, and divide by the number of forecasted periods. In regression, this calculation can be demonstrated by substituting x values into the forecast equation to get predicted y values and then comparing them with the actual values.
Step-by-step explanation:
The student is asking about the biasedness of the naïve forecast method in the context of a regression analysis commonly used in statistical prediction. Unfortunately, the student didn't provide specific data to demonstrate how to calculate bias using the naïve forecast.
However, the naïve forecast method typically assumes that the next forecast value will simply be equal to the last actual observed value. To calculate the bias of such forecasts, you would typically subtract the actual values from the forecast values for the relevant period, sum these differences, and then divide by the number of forecasted periods.
In the context of the regression example given, suppose we use the equation ŷ = -173.51 + 4.83x for prediction and have actual values to compare against the predicted ones. If x represents the independent variable (like time or some other factor), you would substitute the actual x values into the equation to get your forecasted y values (ŷ).
Then for each period, subtract the actual y value (observed) from the forecasted ŷ value, sum these differences, and divide by the number of periods to find the bias of your naïve forecast method.
For example, if your regression equation is ŷ = 101.32 + 2.48x, and you want to predict sales on day 60 and day 90, you would substitute x = 60 and x = 90 into the equation to find ŷ for those days. If you also had the actual sales figures for those days, you could then calculate the bias by comparing the differences between the predicted and actual figures.
The complete question is: Using the data below, calculate the bias based on using the naïve forecast method. Week Time Series Value 5.00 10.00 11.00 9.00 2 3 4