suggestion assumes the absence of the few strong variables or the irrelevant variables in the dataset

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

·         If the selection criteria is based on the p-value then the choice for the significance level is either 0.05 or 0.10

·         The optimum value of the significance level to decide which variable to be included in the model

·         This suggestion assumes the absence of the few strong variables or the irrelevant variables in the dataset

·         There is also the strong recommendations for using the p-value in the range of 0.15-0.20, although using the higher significance level has the disadvantages  that some un-important variables may be included in the model.

·         However we believe that the higher significance level for the variable selection should be considered so that the important variables related to the outcome are not missed and to avoid deleting the less significant variables that has a practical meaning .

2.2 AIC:

·         AIC is the tool for the model selection which compares the different models.

·         AIC attempts to select the model by balancing underfitting and overfitting

·         Including too few variables often fails to capture the true relation and too many variables creates a problem.

·         A model cannot precisely represent the true relation that exist in the data as there is some information loss in estimating the true relation through modelling .

·         AIC tries to estimate the relative information loss compared with the other candidate models.

·         Quality of the model is believed to be with the smaller information loss and it is important to select the model that best minimises that loss.

 

·         Among the available models for the specific data, the model with the minimum AIC is the best.

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