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A mathematical technique used to explain and/or predict. The general form is Y = a + bX + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope, and u is the regression residual. The a and b are chosen in a way to minimize the squared sum of the residuals. The ability to fit or explain is measured by the R-square.

Regression Analysis

In statistics, the analysis of variables that are dependent on other variables. Regression analysis often uses regression equations, which show the value of a dependent variable as a function of an independent variable. For example, a regression could take the form:

y = a + bx

where y is the dependent variable and x is the independent variable. In this case, the slope is equal to b and a is the intercept. When plotted on a graph, y is determined by the value of x. Regression equations are charted as a line and are important in calculating economic data and stock prices.


(1) A statistical technique for creating a mathematical equation to explain the relationship between known variables so that the model can be used to predict other variables when one has insufficient data. Multiple regression analysis is the basis of computerized automatic valuation models (AVM) employed instead of appraisals by many mortgage lenders. (2) An appraisal principle that if properties of relatively unequal value are located near each other, the one with the lower value will depress the value of the other. (3) A withdrawal of the sea from the land due to an uplift of the land or a drop in sea level.

References in periodicals archive ?
is a selective regression function that represents an estimate of the theoretical regression function.
If the regression function is determined, it is necessary to check whether the regression function corresponds to the data.
If y = fdr(t)--the found regression functions, then the extrapolation operation shall be
Using the regression function in score 1, we generated two scenarios by varying the regression coefficients.
The application of Lagrangian multipliers and Karush-Kuhn-Tucker (KKT) conditions to (4) finally leads to the following general form of the SVR-based regression function [18]:
The regularization parameter ([gamma]) controls the penalty imposed to data points that deviate from the regression function.
1, C = 10 and the linear kernel, construct support vector regression, we can get regression function [[phi].
Table 5: Estimated Regression Function of Wheat Production (1980/81 to 2010/11)
In addition, through adjusting constants for LS-SVM model (Table 5), the article reduces error and strengthens the smooth degree of the regression function as soon as possible.
Vedder and Gallaway (1998) use a single square regression function to estimate that the optimum government size of the U.