Final answer:
The MAPE is calculated using actual and forecast values, which are not provided in the question. Thus, we cannot accurately compute the MAPE without the correct data. An increase in sample size can reduce sampling error, but this is unrelated to the calculation of the MAPE.
Step-by-step explanation:
The Mean Absolute Percentage Error (MAPE) is a measure used to assess the accuracy of a forecast system. The MAPE calculates the absolute difference between the actual and the forecast values, divides by the actual values to get a percentage, and then computes the mean of those percentages. However, the information provided does not give us actual and forecast values; it talks about error bounds and confidence intervals, which are not directly used in calculating MAPE. Without the actual and predicted values, we cannot compute the MAPE. It appears there might be some confusion or missing information in the question presented. For a correct MAPE calculation, we need specific actual values (the real observed values) and their corresponding forecast values (the predicted values).
To calculate the MAPE, the steps are as follows:
- Subtract the forecast value from the actual value for each data point and take the absolute value of each to find the percentage error.
- Divide the absolute error by the actual value to convert it into a percentage.
- Sum all the individual percentage errors.
- Divide by the number of observations to find the mean.
Note that increasing the sample size can lower the sampling error, and a percent uncertainty can be calculated as in the case of the measuring tape example given, by dividing the error of 0.50 cm over a 20 m length.