How Dummy Variables Should Be Modeled In A Linear Regression Model?
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I've a cross sectional model where I want predict number of users that take specific service, to make it I've many variables but have specifically two nominal: isWorkday(0 or 1) and weeday(1,2,3,...,7). When I make the model, taking into account the two variables, generates high multicollinearity. So I've delete one of them, so what's better have many dummies (weeday) or less dummies (isWorkday).
linear-regression prediction dummy-variables
asked yesterday
David SalgadoDavid Salgado
82
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David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I've a cross sectional model where I want predict number of users that take specific service, to make it I've many variables but have specifically two nominal: isWorkday(0 or 1) and weeday(1,2,3,...,7). When I make the model, taking into account the two variables, generates high multicollinearity. So I've delete one of them, so what's better have many dummies (weeday) or less dummies (isWorkday).
linear-regression prediction dummy-variables
asked yesterday
David SalgadoDavid Salgado
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago
add a comment |
$begingroup$
I've a cross sectional model where I want predict number of users that take specific service, to make it I've many variables but have specifically two nominal: isWorkday(0 or 1) and weeday(1,2,3,...,7). When I make the model, taking into account the two variables, generates high multicollinearity. So I've delete one of them, so what's better have many dummies (weeday) or less dummies (isWorkday).
linear-regression prediction dummy-variables
asked yesterday
David SalgadoDavid Salgado
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've a cross sectional model where I want predict number of users that take specific service, to make it I've many variables but have specifically two nominal: isWorkday(0 or 1) and weeday(1,2,3,...,7). When I make the model, taking into account the two variables, generates high multicollinearity. So I've delete one of them, so what's better have many dummies (weeday) or less dummies (isWorkday).
linear-regression prediction dummy-variables
linear-regression prediction dummy-variables
asked yesterday
David SalgadoDavid Salgado
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
David SalgadoDavid Salgado
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
David SalgadoDavid Salgado
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
David SalgadoDavid Salgado
82
asked yesterday
David SalgadoDavid Salgado
82
82
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
David Salgado is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago
add a comment |
1
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago
1
1
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Since your task is to predict something, the better variable is the one that gives you a higher prediction accuracy. So you can simply test both and choose the one with which your model performs better.
However, I would suggest considering to engineer your own feature that incorporates information of both variables. For example, you could create three dummy variables: workday, weekend and holiday and include two of them into your model (to prevent falling into the dummy variable trap). Another option would be to only include the interaction terms between isWorkday and weekday.
answered 19 hours ago
georg_ungeorg_un
10818
$endgroup$
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
add a comment |
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1 Answer
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$begingroup$
Since your task is to predict something, the better variable is the one that gives you a higher prediction accuracy. So you can simply test both and choose the one with which your model performs better.
However, I would suggest considering to engineer your own feature that incorporates information of both variables. For example, you could create three dummy variables: workday, weekend and holiday and include two of them into your model (to prevent falling into the dummy variable trap). Another option would be to only include the interaction terms between isWorkday and weekday.
answered 19 hours ago
georg_ungeorg_un
10818
$endgroup$
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
add a comment |
$begingroup$
Since your task is to predict something, the better variable is the one that gives you a higher prediction accuracy. So you can simply test both and choose the one with which your model performs better.
However, I would suggest considering to engineer your own feature that incorporates information of both variables. For example, you could create three dummy variables: workday, weekend and holiday and include two of them into your model (to prevent falling into the dummy variable trap). Another option would be to only include the interaction terms between isWorkday and weekday.
answered 19 hours ago
georg_ungeorg_un
10818
$endgroup$
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
add a comment |
$begingroup$
Since your task is to predict something, the better variable is the one that gives you a higher prediction accuracy. So you can simply test both and choose the one with which your model performs better.
However, I would suggest considering to engineer your own feature that incorporates information of both variables. For example, you could create three dummy variables: workday, weekend and holiday and include two of them into your model (to prevent falling into the dummy variable trap). Another option would be to only include the interaction terms between isWorkday and weekday.
answered 19 hours ago
georg_ungeorg_un
10818
$endgroup$
Since your task is to predict something, the better variable is the one that gives you a higher prediction accuracy. So you can simply test both and choose the one with which your model performs better.
However, I would suggest considering to engineer your own feature that incorporates information of both variables. For example, you could create three dummy variables: workday, weekend and holiday and include two of them into your model (to prevent falling into the dummy variable trap). Another option would be to only include the interaction terms between isWorkday and weekday.
answered 19 hours ago
georg_ungeorg_un
10818
answered 19 hours ago
georg_ungeorg_un
10818
answered 19 hours ago
georg_ungeorg_un
10818
answered 19 hours ago
georg_ungeorg_un
10818
10818
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
add a comment |
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
1
1
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
I get it. But if it's the case where I've a dummy variable for isWorkday and six for the days of the weeks(isTuesday,isWednesday,isThurday, isFriday,isSaturday and isSunday) and both in the multicollinearity tests generate conflict , which dummy I should choose isWeekDay or the six dummies?. Remember that I can make a relationship with two variables (multicolineality): isTuesday,isWednesday,isThurday and isFriday mapped to isWorkday(1) and isSaturday and isSunday to isWorkday(0).
$endgroup$
– David Salgado
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
To make a decision, you first test both options. Case 1: If isWorkday gives you better prediction results, you choose isWorkday. Case 2: If the six weekday-variables give you better prediction results, you choose the six weekday-variables. Case 3: If the six weekday-variables perform equally good as isWorkday, you choose isWorkday, because in this case, you end up with a smaller model.
$endgroup$
– georg_un
8 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Oh, I understand thanks, with that you tell me I'm also convinced that this would be the way to proceed.
$endgroup$
– David Salgado
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
$begingroup$
Awesome, glad I could help.
$endgroup$
– georg_un
7 hours ago
add a comment |
David Salgado is a new contributor. Be nice, and check out our Code of Conduct.
David Salgado is a new contributor. Be nice, and check out our Code of Conduct.
David Salgado is a new contributor. Be nice, and check out our Code of Conduct.
David Salgado is a new contributor. Be nice, and check out our Code of Conduct.
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1
$begingroup$
Have you tried both? Usually when faced with this kind of dilema we perform tests with our available options, unless the amount of tests needed create a effort in which the experiment is not worth of.
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
@PedroHenriqueMonforte you are right, see what I answered next, and I know that all possible models should be tried, but the question is addressed to multicollinearity.
$endgroup$
– David Salgado
8 hours ago