regression

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Regression

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.

regression

(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 ?
In other words, if all of the other variables in the variance function were insignificant, the antilog of the estimate for sigma would be the constant variance estimate for the homoscedastic regression function. To the degree that other variables are significant in the variance function, the regression function is heteroscedastic and its disturbance variance depends on these measures.
Types of accommodation structures) would lead to obtaining regression functions with more representative parameters.
This result was obtained with both direct measures of perceived distance, verbal report and the distance to the stopping points in the indirect blind walking task; the ratios of the slopes of the regression functions (RFOV/control) were 0.83 and 0.86 for verbal report and blind walking, respectively.
In general, the regression function results in Table 3 conform rather closely to our a priori expectations and to results found in other studies.
The regression function m(*) takes the form: m(x) = [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] dx, if f(x) > 0 and the marginal density of f(x, y) becomes: f(x) = [integral]f(x, y)dy.
Consider first the linear regression function's estimates.
For the No AIDS group, the regression function was: Older test item time = [(1.04) x younger test item time + .04], with [r.sup.2] = .99.
However, when the parametric regression function is linear in parameters, then the minimisation problem has an explicit solution.
Running the regression function produces output as shown in Table 3.
The factors derived from the semilogarithmic regression function are additive; that is, when there are multiple structural changes to a housing unit, the regression factor for the change is the total of the separate factors for the different changes.
Following this shape regression idea, various methods try to model a regression function that directly maps the appearance of images to landmark coordinates without the need of computing a parametric model.
The ultimate goal of the support vector regression is to find a regression function f: [R.sup.D] [right arrow] R: