Question: Smoothing reduces the random variation in data producing a sequence
Smoothing reduces the random variation in data, producing a sequence that reveals the systematic trend in the data. Shouldn’t we build models from the smoothed data, which have less random noise, rather than from the original data? Explain why this is, or is not, such a good approach to building a model for forecasting.
Relevant QuestionsA video game was previewed to a group of 30 teenagers. The teens were asked to rate the quality of the graphics and indicate if the game was too violent. (a) Identity whether the data are cross sectional or a time ...Many auto regressions are mean-reverting. Mean-reverting means that the forecasts eventually tend back to (revert to) the mean of the time series. For example, a manager uses an AR(1) model to predict sales next week using ...The following timeplot charts the value (in millions of dollars) of inventories held by Dell, Incorporated (the computer maker). This time series is quarterly. The scatterplot on the next page graphs the value of inventories ...These data measure hourly compensation in the US manufacturing sector from 1987 through 2011. The data are assembled by the Bureau of Labor Statistics from surveys. The value of the index was set to 100 in 2005, so the data ...If you operate a refinery and need to buy crude oil (and haven’t made a prior arrangement that locked in a lower price), the spot price for crude oil determines what you can expect to pay per barrel (42 gallons). The data ...
Post your question