Which one of the following is used to determine accuracy in forecasting? Group of answer choices Biasness Forecast as a percent of actual Linear regression Randomness Mean absolute deviation

All of the options mentioned can be used to determine the accuracy of forecasting in different ways. However, the most commonly used methods are:

1. Mean Absolute Deviation (MAD): This is a measure of the average magnitude of errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables.

2. Biasness: This refers to consistent or systematic errors that cause the forecasts to be consistently higher or lower than the actual values. It's a measure of a systematic error that can cause under- or over-forecasting.

3. Forecast as a percent of actual: This is a relative measure of error. It's often used in sales forecasting where it's important to understand the size of the error relative to the size of the actual demand.

4. Linear Regression: This is a statistical tool used to predict future values from past values. In the context of forecasting, the accuracy of the linear regression model would be determined by how well the predicted values match the actual values.

5. Randomness: This refers to the unpredictability of data points. In the context of forecasting, a high degree of randomness may indicate a lower level of accuracy, as it's harder to identify underlying patterns and trends in the data.

So, all of these can be used to determine the accuracy of forecasting, depending on the specific context and requirements of the forecast.