Question

T/f: The mean absolute deviation is more sensitive to large deviations than the mean square error.

T/f: A smoothing constant of 0.1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a value of 0.3 will.

T/f:An advantage of the exponential smoothing forecasting method is that more recent experience is given more weight than less recent experience.

T/f: Linear regression can be used to approximate the relationship between independent and dependent variables.

T/f:"Forecasting techniques such as moving-average, exponential smoothing, and the last-value method all represent averaged values of time-series data."

T/f: The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered.

Answer 1

Two of the most commonly used forecast error measures are mean absolute deviation (MAD) and mean squared error (MSE). MAD is the average of the absolute errors. MSE is the average of the squared errors. Errors of opposite signs will not cancel each other out in either measures. However, by squaring the errors, MSE is more sensitive to large errors. Either MAD or MSE can be used to compare the performance of different forecasting techniques. The best technique is the one that yields the lowest MAD/MSE. - Hence the statement in Question is FALSE.

A smoothing constant of 0.1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a value of 0.3 will. - FALSE

A weighted moving average allows unequal weighting of prior time periods. The sum of the weights must be equal to one. Often, more recent periods are given higher weights than periods farther in the past. Exponential smoothing puts substantial weight on past observations, so the initial value of demand will have an unreasonably large effect on early forecasts.Hence the statement in question is FALSE.

In a simple linear regression model, the correlation coefficient not only indicates the strength of the relationship between independent and dependent variable, but also shows whether the relationship is positive or negative. Hence the statement in the question is TRUE.

Forecasting techniques such as moving-average, exponential smoothing, and the last-value method all represent averaged values of time-series data. TRUE

The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered.. TRUE