February 2016
Intermediate to advanced
480 pages
219h 58m
English
We now deal with the same mathematical model that we saw earlier, the least-squares method. But we use any potential “cause-and-effect” variable as x.
Unlike time-series forecasting, associative forecasting models usually consider several variables that are related to the quantity being predicted. Once these related variables have been found, a statistical model is built and used to forecast the item of interest. This approach is more powerful than the time-series methods that use only the historical values for the forecast variable.
Many factors can be considered ...