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Linear regression trend lines are plotted for each site class.
Blue line indicates linear regression trend and grey area is 95% confidence interval for the regression line.
In this study the accuracy is evaluated by comparison with the global Terra MODIS NDVI (MOD13C2 Collection 5) data using linear regression trend analysis.
A non-iterative processed empirical distribution model (NPEDM) and a linear regression trend model (LRTM) are proposed to predict sediment distribution.
Linear regression trend lines were then generated and graphed in Figures 2A and 2B.
These values were plotted against the corresponding divergence times for the compared species, and the linear regression trend line was estimated from each group, as shown in Figure 2. From the slope of the lines and the r2 values we are able to compare the rate of proteins evolution and its constancy over time, respectively.
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Furthermore, the classical and simplest RK, i.e. using a Least Squares Linear Regression (LR) trend built with only continuous covariates, outperformed all standalone trend models.
Linear regression assesses trend.
To evaluate visual field (VF) progression and rate of glaucomatous VF loss in patients with primary angle-closure glaucoma (PACG) using pointwise linear regression (PLR) trend analysis.
Trends were analyzed with linear regression; those trends with p-values = 0.05 were deemed significant.
After transformation into standardized residuals by linear regression, these trends disappeared (Additional file 1: Figure S1B).
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