Used to define a functional relationship between two or more correlated variables.
The relationship is developed from observed data where the independent variable is used to predict the dependent variable.
Linear regression refers to a special class of regression where the relationship between the variables is assumed to be represented by a straight line.
Linear regression analysis is useful for long-term forecasting of major occurrences and aggregate planning.
The major restriction in using linear regression forecasting is that past data and future projections are assumed to fall around a straight line.
Linear regression is used for both time series forecasting and for causal relationship forecasting. When the dependent variable changes as a result of time, it is referred to as time series analysis.
If the dependent variable changes due to the change in the independent variable, it is a causal relationship.
A measure of how well the data fits the regression line can be determined by calculating the standard error of estimate.
Standard error of estimate: reflects how widely the errors are dispersed around the regression line.
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