Because obesity and weight regulation is complex, a panel of predictors covering various processes performs better than one or two predictors on the prediction of weight change after weight loss.
To further clarify the interpretation of the interaction terms as estimates of the effects of the predictors on the amount of change over time, one can refer to the estimates in Table 4.
In multinomial logistic regression analyses the influence of several predictors on the class membership was examined.
Through gbm function implementation, the relative influence is calculated to show the most influential predictors on the response (core permeability).
OLS regressions were conducted with all prefecture predictors on the estimated Gini from the random slope model.
A multilevel analysis investigated differences in achievement with experimental conditions as predictors on the class level and gender, language spoken at home, and pretest scores as predictors on the student level.
Multiple logistic regression analyses examined the effects of 22 predictors on the course of depression (onset, improvement, stability).
However, few studies explore the effects of knowledge-based predictors on the comparative choice of employing acquisitions versus alliances for sourcing external knowledge.
In this regression model, EDAS Usage and System Experience served as predictors (among three other predictors) on the regressand Average Fuel Consumption.
This model removes the effect of those time-invariant characteristics, so it can be assessed the net effect of the predictors on the outcome variable.
A more comprehensive picture of the effect of the predictors on the response variable can be obtained by using quantile regression.
