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Hi all,

I am from engineering background. I would require your help in certain modeling concepts. Your help would be greatly appreciated!

Following are my few questions...

- If a variable which is important from business standpoint has a p-value of 0.5, then should it be considered in the model? If Yes, then wouldn't it make the model coefficients unstable?
- Should I standardize the variables before building a logistic regression model? If Yes, is there a commonly followed approach?
- I am planning to develop a logistic regression to rate the employees as good or bad. The model includes variables such as his innovation score, #papers published, salary, Training cost, etc. First two are kind of assets to the company and the next two are kind of liabilities. Should I explicitly make the model understand this by considering the liabilities as negative values?
- I have two independent variables in my LR model. Var1 has levels 'A' and 'B'. Var2 has levels 'X' and 'Y'. Of the entire dataset, there are 30% observations with Var1 as 'A' and Var2 as 'X', 35% observations with Var1 as 'A' and Var2 as 'Y', 30% observations with Var1 as 'B' and Var2 as 'X', 5% observations with Var1 as 'B' and Var2 as 'Y'. The number of observations with Var1 as 'B' and Var2 as 'Y' are far too less compared to other combinations. Is this skewness in data going to affect my results? If so, how should I rectify this?

Tags: Logistic, Modeling, P-Value, Regression, Statistical

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