A statistical technique used to explain or predict the behavior of a dependent...
The equation of the regression function. It may be of any functional form and the terms may be orthogonal or not.
Description of the â€œbest fitâ€ summary of the relationship between pairs (or more complex sets) or data. Like the mean, the regression line does not describe actual data (except in trivial cases); the regression equation is convenient summary. It is constructed as the line that minimizes the sum of differences between each point and the regression line in a scattergram. In the most common, straight line or linear case, the point estimate of the mean is replaces by a â€œrunningâ€ point with the formula y = c + b * x, where y is the dependent or predicted value in each pair of observations and x is the independent or predictor value in each pair. C in this equation is called the intercept and a is the slope of the regression line. [See intercept, prediction, residual, slope, standard error
the equation representing the relation between selected values of one variable (x) and observed values of the other (y); it permits the prediction of the most probable values of y
A regression equation allows us to express the relationship between two (or more) variables algebraically. It indicates the nature of the relationship between two (or more) variables. In particular, it indicates the extent to which you can predict some variables by knowing others, or the extent to which some are associated with others. (From the Department of Statistics of the University of Glasgow, http://www.stats.gla.ac.uk/)
A prediction equation which allows values of inputs to be used to predict the value of outputs.
An equation that describes the average relationship between a dependent variable and a set of explanatory variables.