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Table 1 describes the results of the model (see Text S1 for the partial regression plot).
As an alternative means of assessing a difference in eGFR between high and low risk groups, a partial regression plot was constructed, whereby the plot displayed the relationship of eGFR against time, after adjusting for the significant baseline covariates.
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No relationship was apparent in other partial regression plots.
Of the five predictors, only two showed noticeable patterns in the partial regression plots.
Linearity was assessed through visual inspection of partial regression plots and a plot of studentized residuals against the predicted values.
Three attitudinal factors were significant for predicting Help from Instructor, not two as expected from observing the partial regression plots.
Preliminary regression analyses revealed relatively high tolerances for all language features within each set of categories (from 0.412 to 0.626 for dictionary categories and from 0.497 to 0.665 for more modern grammar categories), indicating that multicollinearity would not impede interpretation and partial regression plots indicated linear relationships between the language feature categories.
Standard regression diagnostics were assessed, including standardised residual plots, partial regression plots, and variance inflation factors.
We used partial regression plots to graphically display the results of the multiple GLS analyses [ 74].
Leverage plots and partial regression plots were examined to assess any influence individual observations might have on model coefficients.
Partial regression plots were inspected for outliers and heteroskedacity, and residuals tested for normality using the Shapiro Wilkes test.
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