Exam 15: Demand Management and Forecasting

The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts.

A restriction in using linear regression is that it assumes that past data and future projections fall on or near a straight line.

In most cases, demand for products or services can be broken into several components.Which of the following is considered a component of demand?

Because the factors governing demand for products are very complex, all forecasts of demand contain some error.

Which of the following forecasting methods uses executive judgment as its primary component for forecasting?

Which of the following is not one of the basic types of forecasting?

In the simple exponential smoothing forecasting model you need at least 30 observations to set the tracking alpha.

Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment.

In business forecasting, what is usually considered a medium-term time period?

Which of the following forecasting methods can be used for short-term forecasting?

If a firm produced a product that is experiencing growth in demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be which of the following?

Given a prior forecast demand value of 230, a related actual demand value of 250, and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period?

In a forecasting model using simple moving average the shorter the time span used for calculating the moving average, the closer the average follows volatile trends.

MAD statistics can be used to generate tracking signals.

In business forecasting, what is usually considered a short-term time period?

Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model.

The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1.

We usually associate the word "seasonal" with recurrent periods of repetitive activity that happen on other than an annual cycle.

In decomposition of time series data it is relatively easy identify cycles and autocorrelation components.

Multiple regression analysis uses several regression models to generate a forecast.