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ma17 - Quantitative Forecasting Methods(Associative...

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Quantitative Forecasting Methods (Associative Forecasting) Our forecasting approaches up to this point have relied on time series data. That is, the independent variable was always time. When we are trying to explain a dependent variable using an independent variable other than time, we can use associative forecasting, which is a kind of causal forecasting. For example, sales may be a function of discretionary income, price, advertising, competition, etc. We can use associative forecasting to develop a model that shows how sales vary with these independent variables. Linear regression is the most common associative forecasting technique. We discussed linear regression when we discussed trend lines in time series. We apply the technique in the exact same way we did before, but instead of time as our x value, we use some other independent variable. Evaluating the Accuracy of a Linear Regression Model The standard error of the estimate is also a tool that helps evaluate the accuracy of a linear regression model. A forecast using a linear

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