This is the equivalent in linear regression of the coefficient of determination, R, which is a measure of the variance in the data explained by the linear regression model.
The r2 represents the fraction of the variation explained by a linear regression model.
About 56% of the total grain yield variability in our experiments was explained by a linear regression model with management, altitude and N-fertilizer input.
Approximately 64% of the variance in δDf values was explained by this linear regression on the modeled values for growing season δDp at the feather collection locations (δDf = 0.9523 δDp – 27.8‰), and the slope parameter was significantly different from zero (p = 2×10−16).
Afterwards, other explanatory baseline variables were incorporated to achieve higher adjusted R, i.e. the percentage of the variation explained by the linear regression model.
It appears that PF is the HRQOL dimension most significantly influenced by sociodemographic and disease-related factors, and this is reflected by the high portion of variance explained by the linear regression model (42%).
In addition, the test takes into account the residual sum of squares associated with the calibration curves, which is an estimate of the amount of variation remaining in the experimental data that is not explained by the linear regression model.
The variation in SPSS explained by the linear regressions was lower and P-values were no longer significant (SB: R = 0.02, P = 0.14, df = 74; WG: R = 0.01, P = 0.96, df = 74).
When the experimental results were tentatively adjusted by linear regression, only 58.15% of its total variance was explained.
Linearity of the standard curves and correlation coefficients were obtained by linear regression analyzes.
