Sentence examples for multiple regression demonstrated from inspiring English sources

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Utilising a cross-sectional design to predict BMI, significant forward stepwise multiple regression demonstrated that FA (β = .386) and impulsivity (non-planning) (β = .286) were significant predictors.

Multiple regression demonstrated that these associations were still present even after adjustments were made for medication class, dosing frequency, and patient income (Table  4).

Analysis by stepwise multiple regression demonstrated independent correlations of duration of labour with parity (coefficient of determination R2 = 0.0489, R-adjusted = 0.039, P < 0.05) and with CR (P < 0.05).

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After controlling for age, sex and injury severity, multiple logistic regression demonstrated that a low TAT index was not associated with mortality (P = 0.11) (Table  4).

Multiple logistic regression demonstrated cTnI status to be an independent predictor of left ventricular dysfunction with an adjusted odds ratio of 10.34 (2.31 46.21) and a P value of 0.002.

After controlling for age, sex, and injury severity, multiple logistic regression demonstrated that increased muscle index was significantly associated with decreased mortality (OR per unit muscle index = 0.93, 95% CI: 0.875-0.997, P = 0.025).

Multiple linear regression demonstrated that higher HbA1c [ B (SE) = 3.648 (1.789); p = 0.043] and hsCRP [ B (SE) = 0.439 (0.196); p = 0.027] were significantly associated with elevated serum ANGPTL2 concentrations in these individuals (Additional file 1: Table S1).

Consistent with the results of univariate analysis, multiple logistic regression demonstrated that females were less likely to have severe ADRs than males (table VI: adjusted OR 0.81; 95% CI 0.72, 0.92).

Multiple regression analyses demonstrated that components of task value predicted intentions to attend graduate school.

Multiple regression analysis demonstrated that public expenditure on education was the only parameter that was an independent predictor for the radiological research activity.

Multiple regression analysis demonstrated that public expenditure on education was an independent predictor of radiological research activity (P < 0.001), whereas the year, GDP and health expenditure did not reach statistical significance (P > 0.05).

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