# regression curve

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 Noun 1 regression curve - a smooth curve fitted to the set of paired data in regression analysis; for linear regression the curve is a straight lineregression linestatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parametersregression analysis - the use of regression to make quantitative predictions of one variable from the values of anothercurve - a line on a graph representing data
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If all the values are on the regression curve, it results that [R.sup.2]=1 and therefore data matching is perfect for the calculated regression curve.
Ambulatory Arterial Stiffness Index: The slope of the regression curve obtained from marking the SBP and DBP values from 24-hour blood pressure records of each patient on a (x, y) plane was calculated.
Truncated spline approach is used to solve spatial analysis problems whose regression curve is unknown .
For the second model, the 18 points in the visual field located around the previous 16 were added to the regression curve, so that the model studied the function-structure relationship between 34 central points in the visual field and the complete macular grid (Figure 1(c)).
3-12), and (5) statistical models for determining values and concentrations of physico-chemical characteristics (y) of suspended particulate matter in the air with determined values/concentrations of physico-chemical characteristics of inert matter at new location (x), regression constant (a) and value of slope (b) of the regression curve, at any new locations (Table-3).
The regression equation of the ascending segment of different contents of fiber incorporation is shown in Figure 7, in which the cubic regression curve equation was obtained by the simulation of the ascending segment of the axial compressive stress-strain curve, and it was solved to obtain the secant modulus at 0.4 times, as seen in Table 6.
The regression curve in Figure 1(c) shows that [P.sub.d][sup.Ref] is well correlated with earthquake magnitude.
The final penalty regression curve is computed by minimizing the cost function E as follows:
In Fig.8 a comparison between the absolute values of the error % of correlation data between the polynomial regression curve and the values obtained by different correlations are presented.
Each time point (x) is associated with a cost point to run the nonlinear regression curve fitting.

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