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 ?
It can be observed that ASHRAE Handbook and regression equation results are in octave bands, whereas measured and FEM results are in 1/3-octave bands.
m) of the variables and for the free term b in the regression equation indicates whether or not the relevant variables or the free term are significant.
To the best of author knowledge, until now, no study has been published to determine the reliability of the two mentioned methods, so the aim of the present study was to compare the sum mesiodistal widths of the maxillary and mandibular incisors using Tonn's and Abhi's methods with the actual mesiodistal widths and, at the same time, to formulate regression equations to predict the sum mesiodistal widths of the maxillary and mandibular incisors and compare them with the actual and previous methods.
The direct and indirect effects of selected morphological traits on body weight on multiple regression equations were then determined using path correlation analysis.
In order to improve the accuracy of prediction of observed BW from linear body measurement, multiple linear regression equations were developed.
Regression equation along with dental cast is the only solution in such cases if these have a reasonable degree of validity.
We also derived a multiple linear regression equation to predict gestational age from CHL and HC in a whole cohort.
The best regression equation for a calibration curve should have the following characteristics (without distinguishing the subscripts O and UW): (i) intercept b small approaching to zero; (ii) slope m large; and (iii) both [u.sub.b] and [u.sub.m] small.
The regression equation derived from the left foot index of males is given in Table 2.
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.
The slope of the regression line represented the TTTD of AEE, with the y-intercept of this regression equation being considered the ELF of AEE (g/kg of DMI).
The empirical model developed using regression equation includes main factors and first order interaction of all factors.