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Linear regression Model 2 dummy variables of same categorical variables

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Linear regression Model 2 dummy variables of same categorical variables

I am using PROc Reg (Selection = stepwise ) linear regression for banking data which has credit customer information for which we need to predict the factor affecting the usageof credit card. In output of PRoc Reg . My model is giving of the final factors(dummyvariable factors) as 

 

dummy_card1 meaning( amex)

dummy_card4     meaning(discover), (both are types of primary )  refer to screenshot for more clarity

these card are primary credit cards

 

My question is can we have a final model in which we can have 2 dummy variables from same categorical variables .

 

Also , can we reduce the number of variable even after the output of ( selection = stepwise ). ?

 

 

P.s : I am naive in the world of analytics ..Thanx in advance 

 

 


dummydummy.png

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‎03-30-2017 01:34 PM
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Re: Linear regression Model 2 dummy variables of same categorical variables


pathakvishal wrote:

I am using PROc Reg (Selection = stepwise ) linear regression for banking data which has credit customer information for which we need to predict the factor affecting the usageof credit card. In output of PRoc Reg . My model is giving of the final factors(dummyvariable factors) as 

 

dummy_card1 meaning( amex)

dummy_card4     meaning(discover), (both are types of primary )  refer to screenshot for more clarity

these card are primary credit cards

 

My question is can we have a final model in which we can have 2 dummy variables from same categorical variables .

 


Yes, of course you can.

 


Also , can we reduce the number of variable even after the output of ( selection = stepwise ). ?

 


Yes, you can do this too, but I wouldn't, because stepwise provides the result that if finds to be the best fit model. I would however recommend that stepwise isn't highly regarded by most statisticians these days, and perhaps something like PROC PLS might be a better alternative with multiple input variables that are correlated.

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‎03-30-2017 01:34 PM
Trusted Advisor
Posts: 1,613

Re: Linear regression Model 2 dummy variables of same categorical variables


pathakvishal wrote:

I am using PROc Reg (Selection = stepwise ) linear regression for banking data which has credit customer information for which we need to predict the factor affecting the usageof credit card. In output of PRoc Reg . My model is giving of the final factors(dummyvariable factors) as 

 

dummy_card1 meaning( amex)

dummy_card4     meaning(discover), (both are types of primary )  refer to screenshot for more clarity

these card are primary credit cards

 

My question is can we have a final model in which we can have 2 dummy variables from same categorical variables .

 


Yes, of course you can.

 


Also , can we reduce the number of variable even after the output of ( selection = stepwise ). ?

 


Yes, you can do this too, but I wouldn't, because stepwise provides the result that if finds to be the best fit model. I would however recommend that stepwise isn't highly regarded by most statisticians these days, and perhaps something like PROC PLS might be a better alternative with multiple input variables that are correlated.

Occasional Contributor
Posts: 6

Re: Linear regression Model 2 dummy variables of same categorical variables

Thanx Mate !!


PaigeMiller wrote:

pathakvishal wrote:

I am using PROc Reg (Selection = stepwise ) linear regression for banking data which has credit customer information for which we need to predict the factor affecting the usageof credit card. In output of PRoc Reg . My model is giving of the final factors(dummyvariable factors) as 

 

dummy_card1 meaning( amex)

dummy_card4     meaning(discover), (both are types of primary )  refer to screenshot for more clarity

these card are primary credit cards

 

My question is can we have a final model in which we can have 2 dummy variables from same categorical variables .

 


Yes, of course you can.

 


Also , can we reduce the number of variable even after the output of ( selection = stepwise ). ?

 


Yes, you can do this too, but I wouldn't, because stepwise provides the result that if finds to be the best fit model. I would however recommend that stepwise isn't highly regarded by most statisticians these days, and perhaps something like PROC PLS might be a better alternative with multiple input variables that are correlated.


 

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