# regression equation

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 Noun 1 regression equation - the equation representing the relation between selected values of one variable (x) and observed values of the other (y); it permits the prediction of the most probable values of yregression of y on xstatistics - 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 anotherequation - a mathematical statement that two expressions are equal
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References in periodicals archive ?
We used the non-standardized regression coefficient to generate the regression equation. The regression equation is used to accurately predict the values of the levothyroxine dose variable, and its formula is as follows:
Stressed Fish Pond: First regression equation gives the regression of planktonic biomass on temperature of water.
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.
At the bottom of the plot is the orthogonal regression equation, which relates the supplier data to OEM data.
So, the closer [R.sup.2] is to 1, the more regression equation is more appropriate.
One of the most reliable methods of estimating the width of unerupted teeth is the developing of regression equation utilizing other teeth.
The multiple regression equation for weight traits (Y) was calculated as follows: Y = a + [b.sub.1][X.sub.1] + [b.sub.2][X.sub.2] + [b.sub.3][X.sub.3] +......+ [b.sub.i][X.sub.i], where Y is the dependent variable, a is the intercept, [b.sub.i] is the partial regression coefficient, and [X.sub.i] is the independent variable.
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 r-value for correlation of HC with GA was 0.863 and regression equation for calculation of GA from HC was GA (weeks) = 9.2671 + [0.8616 x HC (cm)].
The application of the RR or PCR estimators from the biased estimation methods gave more consistent and safer results instead of the regression equation generated by the LS method.
Apart from the thirty eight chapters covered under seven parts, book also provides an intuitive introduction to regression analysis, review of elementary statistics, determining significance of the regression analysis, multiple regression model, analysis of regression equation, and practical considerations in applying regression analysis under appendices.

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