The predictor variables inserted in the model were subsequently removed one at a time, causing no significant change in results and therefore allowing to exclude any confounding effect of single variables on the model.
A Cox proportional hazard model was used and the effect of single variables was expressed as hazard ratio (HR) with 95% confidence interval (95% CI).
Log-rank test was used to assess the effect of single variable on insertion time.
Effects of single-variable biofeedback on wheelchair handrim biomechanics.
Following a descriptive analysis using cross-tabulation and distribution of single variables, linear and logistic regression analysis was used for determination of effect estimates.
At the conclusion of the stepwise analysis, we visualized the effects of single independent variables (water parameters) on individual response variables (averaged for each spring) using simple linear regression.
Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED.
It is well known that, in general, the ignition attitude of a dust depends on several factors, such as the nature of the chemical, the particle size, moisture content, etc., but there is still a lack of knowledge on the effect of the single variables.
Therefore, the second step of the model building process involved fitting a series of separate flexible spline-based models, each of which tested the time-dependent and, for continuous variables, non-linear effects of a single variable, while adjusting for the PH-and-linear effects of all other variables selected at step 1 (Binquet et al, 2008).
In the lab, we carefully design experiments that attempt to pinpoint the effects of a single variable (like only varying the pH but not pH and temperature), cautious to avoid any bias, and ultimately prove to ourselves that we did a good job.
