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The method started with all independent variables (Equation 1) entered in a linear regression, and then step-by step variables with smallest t statistic and p-value of at least 0.100 were removed to get the most significant model.
Go step by step.
In backwards elimination, the method first includes all variables and step-by-step eliminates variables until no omitted variable would have contributed significantly to the model.
As it was previously mentioned, the step-by-step kinematic variables require higher muscle control, and this could be the reason of the high correlation of this variable with the functionality.
This method has the ability to choose, step by step, the variables that are responsible for the largest portion of the explained variance.
We then eliminated step by step all variables which did not contribute to the model in a statistically significant way (p > 0.1).
Table 4 shows the optimal subset of regressors obtained step-by-step by the variable selection procedure.
A step-by-step backward elimination of these variables from the model was used to identify those to be retained in the model.
In these models, all univariate significant variables were included and after step-by-step elimination of the least significant variable while observing less than 10% change in regression coefficient, a final model was reached for both recurrence and breast cancer-related death.
The reason the analog computation model can't handle large problems is it gives up step-by-step discrete-time operation, instead allowing variables to evolve smoothly in continuous time.
For all models, Table 7 shows the predictor variables entered step-by-step or removed during the stepwise feature selection process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com