regression line

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Related to regression line: regression analysis, correlation coefficient

regression line

[ri′gresh·ən ‚līn]
(statistics)
A linear regression equation with two or more variables.
References in periodicals archive ?
The thin dashed line represents the line of perfect agreement (y = x), the thick dashed line represents the Passing-Ba-block regression line (mean bias), the dark gray shaded area represents the acceptance limits within the inherent imprecision of both analytical methods, the light gray shaded area represents the clinically allowable error limits.
As can be seen in Figure 4, with axis intercept values of 69.4 (100/20) and 70.1 (100/100) as well as rates of strength loss of 8.73 (100/20) and 9.03 (100/100) being very similar, the regression lines are very close to each other.
Log concentration-mortality regression line for the activity of flaxseed oil on Tribolium castaneum.
However, because of the flat slope of the regression line (0.37), the Lin concordance coefficient was much weaker ([r.sub.c] = 0.444), indicating substantial discordance between the 2 assays.
1), one can state that the measured plots are better approximated by the regression curve (parabola) than by the regression line.
Note that the minimum value for SE is zero, which would imply that all of the actual observations ([Y.sub.i]) are on the regression line.
To investigate the differences between age brackets in the obtained regression lines for each of the four types of experimental task, covariance analysis of [M.sub.m] was performed for each experimental task using [M.sub.f] as the covariant.
In such a case, the best-fitting regression line through the data on the right (treatment) side of the cutoff will have a positive slope and a negative intercept.
Note that the slope of the regression line significantly increased upon fly ash replacements, thereby increasing the torque viscosity.
Student's t-test was used to compare the difference of HR, MAP, slopes of the regression lines, and parameters of the baroreceptor function curves between groups.
He explains the need for more than one random-effect term when fitting a regression line and in a designed experiment; the evaluation of the variances of random-effect terms; interval estimates for fixed-effect terms in mixed models; estimation of random effects in mixed models using Best Linear Unbiased Predictors; more advanced mixed models for more elaborate data sets; the use of mixed models for the analysis of unbalanced experimental designs; extending mixed modeling; and why the criterion for fitting mixed models is called the REsidual Maximum Likelihood (REML).

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