Exam 3: Forecasting

Regression is a functional relationship between two or more correlated variables, where one or more variables are used to predict a single variable of interest.

In most cases, demand for products or services can be broken down into several components. Which of the following is not considered a component of demand?

Trend lines are usually the last things considered when developing a forecast.

Heavy sales of umbrellas during a rain storm is an example of which of the following?

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

In general, which forecasting time frame is best to detect general trends?

In forecasting, RSFE stands for "running sum of forecast errors."

Decomposition of a time series means identifying and separating the time series data into its components.

MAD statistics can be used to generate tracking signals.

Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

As a consultant you have been asked to generate a unit demand forecast for a product for year 2018 using exponential smoothing. The actual demand in year 2017 was 750. The forecast demand in year 2017 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2018 forecast value?

The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast.

In general, which forecasting time frame compensates most effectively for random variation and short term changes?

It is difficult to identify the trend in time series data.

Which of the following forecasting methodologies is considered a causal forecasting technique?

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.

In the weighted moving average forecasting model the weights must add up to one times the number of data points.

Which of the following forecasting methodologies is considered a time series forecasting technique?

In general, which forecasting time frame best identifies seasonal effects?

Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range?