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Res 342 Week 5 DQ 2

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Week 5 DQ 2: Why is the trend commonly thought to be the most important
component of forecasting? How is the linear trend method related to linear
regression? Why are simple moving averages and weighted averages commonly
used forms of forecasting?
Trend forecasting is the most used methods of forecasting. It is often the starting point
for forecasters who after doing a trend analysis, modify the future trends discovered using
various other methods and information to arrive at a better forecast. Trend analysis identifies
and maps past and present experience along the path of change traveled over time and when
extended, forecasts are likely heading for the future. Trend forecast are based upon the
assumption that the future will unfold at about the same rate of change, direction, and
magnitude of growth or decline as it did in the past. Trend forecasts most often give a least
likely prediction for the long range future they predict because future change may also be
steered by other factors and other forces. Trend forecasting can be quantitative and qualitative.
Linear regression is related to the linear trend method in that of all the practical uses of
linear regression, when used to predict or forecast, linear regression can be used to fit a
predictive model to an observed data by set of Y and X values. After developing such a model,
if an additional value of X is then given without its accompanying value of Y, the fitted model
can be used to make a prediction of the value Y. The most common method for fitting a
regression line is the method of least squares. The least squares method calculates the best
fitting line for the observed data by minimizing the sum of the squares of the vertical deviation
from each data point to the line, then its vertical deviation is 0. Because the deviations are first
squared, then summed, there are no cancellations between positive and negative values.
Simple moving averages and weighted averages are used in forecasting to calculate an
average of annual demands from a specified number of prior periods, each new forecasts
drops the demand in the oldest period and replaces it with the demand in the most recent
period; so the data in the calculation moves over time, a slightly more complex method for
smoothing a raw time series is to calculate a weighted moving average by first choosing a set
of weighting factors and then using these weights to calculate the smoothed statistic.

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Week 5 DQ 2: Why is the trend commonly thought to be the most important component of forecasting??How is the linear trend method related to linear regression??Why are simple moving averages and weighted averages commonly used forms of forecasting? Trend forecasting is the most used methods of fore ...
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