Regression equation

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Regression equation

An equation that describes the average relationship between a dependent variable and a set of explanatory variables.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.

Regression Equation

In statistics, an equation showing 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. 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.
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References in periodicals archive ?
In ASHRAE RP-556, summarized in the ASHRAE publication Algorithms for HVAC Acoustics, Reynolds and Bledsoe (1990) analyzed the measured insertion loss data for lined rectangular ducts procured in measurement campaigns by Kuntz (1987), Kuntz and Hoover (1987), and Machen and Haines (1983), and developed a regression equation for each octave band center frequency from 63 Hz to 8000 Hz.
Simple regression analysis to establish the regression equations that can be used to predict the sum of mesiodistal widths of maxillary and mandibular incisors from each other
Stepwise multiple regression analysis can identify the significance of the partial regression coefficient and gradually remove nonsignificant morphological traits; this method was used to construct multiple regression equations between total body weight and morphological traits.
The aim of this study was to validate the applicability of a regression equation proposed by Melgaco for prediction of mesiodistal width of unerupted canine and premolars in mandibular arch (PSCP) in class II division 1 occlusal relations.
The implications of these regression equations can be understood from the comparison of the uncertainties of the intercept and slope, which are lower for the UWLR (equation (14)) than for the OLR (equation (13)).
Table 7 shows the prediction equations for TPA parameters as determined from regression equations using WBS.
Our results demonstrated that when PV/TV ratio was used as an index, a linear regression equation for individual age was established: Y=69.137-621.200 (PV/TV), R=0.544; the inferred function of male age: Y=64.333-468.811 (PV/TV), R=0.435; the inferred function of female age: Y=76.445-843.186 (PV/ TV), R=0.691.
Multivariate linear regression analysis was performed to formulate regression equation for estimation of length of femur from measurements of various fragments.
The use of skinfolds has even led to derivations of multiple regression equations in diverse populations (15).
Regression co-efficient were solved in Design Expert 7 statistical software, to obtain regression equations and predict the weld zone micro hardness.