multiple linear regression


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multiple linear regression

[¦məl·tə·pəl ¦lin·ē·ər ri′gresh·ən]
(statistics)
A technique for determining the linear relationship between one dependent variable and two or more independent variables.
References in periodicals archive ?
Multiple linear regression analysis illustrated the linear correlation between the concentrations of danshensu and the relaxation effects of Danshen water-extracts on U46619-pre-contracted rat basilar artery with endothelium (R = 0.
3] Multiple linear regression analysis data as the final TIMI frame count of the culprit coronary artery dependent variable CI - confidence interval, CKMB - creatine kinase-MB, EF - ejection fraction, TIMI - Thrombolysis in Myocardial Infarction
The first multiple linear regression model indicated that service quality explained a significant portion of variability in donor satisfaction.
Multiple linear regression analysis or not, it makes no common sense to say that a home with HVTL lines across the front will sell as readily, and at the same price, as an identical home without the unattractive lines.
In this group of students, it was also found in both the correlational analysis and the multiple linear regression analysis that two design characteristics emerged as significant: Objectives and Problem Solving.
This evaluation was carried out using a multiple linear regression of the hourly chilled water consumption ([Q.
The prediction of muscle mass improved with multiple regressions, and the results of these multiple linear regressions with step-wise forward selection for prediction of muscle mass are summarized in Table II.
Most practitioners use multiple linear regression (MLR) first-order models of the form:
They then performed multiple linear regression (Neter & Wasserman, 1974) using as predictors the five climatological variables mentioned above and simple regression models with predictors significant at the 99 percent significance level.
If studies reporting one-year results had reported retention rates consistently, it would be possible to conduct a multiple linear regression using study characteristics to evaluate those characteristics most commonly associated with first year retention.
Multiple linear regression analysis showed that fat mass, body mass index, leptin, and HDL cholesterol were all robust predictors of aortic elastic function in obese individuals.
Table 4 shows the multiple linear regression analysis for IOP and its covariates.

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