Forecasting Errors and Calculations

The video tutorial introduces the concept of moving averages, a technique used to smooth out data by removing outliers and highlighting trends. It explains how to calculate a three-year moving average and discusses the importance of forecast errors, including mean absolute deviation, mean square error, and mean absolute percentage error. The tutorial provides step-by-step calculations and emphasizes the significance of understanding errors in forecasting.

What is the primary purpose of using a moving average in data analysis?

• To decrease data accuracy

• To highlight outliers

• To increase data variability

• To smooth out fluctuations and reveal trends

Which of the following is NOT a possible period for a moving average?

• Nine years

• Six years

• Four years

• Three years

Why can't we calculate the moving average for the first year?

• The demand is too high

• The data is incomplete

• There is no demand data available

• There is no prior data to average

What is the forecasted demand for the fourth year using a three-year moving average?

• 357 units

• 392 units

• 443 units

• 420 units

How is the forecast for the seventh year calculated?

• Using the average of the first three years

• Using the average of the last three years

• Using the average of all available years

• Using the average of the middle three years

What is the definition of a forecast error?

• The average of forecasted values

• The sum of all forecasted values

• The product of actual and forecasted demand

• The difference between actual and forecasted demand

Why can't we calculate the forecast error for the first three periods?

• The forecast data is missing

• The actual data is missing

• The data is too complex

• The periods are not consecutive

What is the mean absolute deviation (MAD) used for?

• To determine the highest demand

• To measure the average error magnitude

• To calculate the average demand

• To find the lowest forecast error

How is the mean absolute deviation calculated?

• By multiplying the sum of errors by the number of periods

• By dividing the sum of errors by the number of periods

• By adding the sum of errors to the number of periods

• By subtracting the sum of errors from the number of periods

What is the significance of taking the absolute value of errors?

• To ensure all errors are positive

• To make calculations easier

• To reduce the number of errors

• To increase the accuracy of forecasts