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logistic regression

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jimjohn

logistic regression

If I have a dichotomous categorical dependent variable (ie the only values it can take are 1=yes and 0=no), and my independent variables are all continuous (not categorical), then is logistic regression the best method to use? or should i be using discriminant analysis in this case? thanks!

Swank, Paul R

Re: logistic regression

If you're going to use discriminant analysis then you need a lot of
data. Logistic would be the best plan.

Paul R. Swank, Ph.D.
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center - Houston

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
jimjohn
Sent: Thursday, June 05, 2008 3:07 PM
To: [hidden email]
Subject: logistic regression

If I have a dichotomous categorical dependent variable (ie the only
values it
can take are 1=yes and 0=no), and my independent variables are all
continuous (not categorical), then is logistic regression the best
method to
use? or should i be using discriminant analysis in this case? thanks!
--
View this message in context:
http://www.nabble.com/logistic-regression-tp17678490p17678490.html
Sent from the SPSSX Discussion mailing list archive at Nabble.com.

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jimjohn

Re: logistic regression

Thanks Paul! Just wondering how many cases would you consider lots,
because some of these data sets I receive are pretty big. And in the
case where I do haev enough data to use discrimant analysis, would you
then recommend I use that instead of logistic? Thanks!

Regards,

Azam

Quoting "Swank, Paul R" <[hidden email]>:

> If you're going to use discriminant analysis then you need a lot of
> data. Logistic would be the best plan.
>
> Paul R. Swank, Ph.D.
> Professor and Director of Research
> Children's Learning Institute
> University of Texas Health Science Center - Houston
>
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
> jimjohn
> Sent: Thursday, June 05, 2008 3:07 PM
> To: [hidden email]
> Subject: logistic regression
>
> If I have a dichotomous categorical dependent variable (ie the only
> values it
> can take are 1=yes and 0=no), and my independent variables are all
> continuous (not categorical), then is logistic regression the best
> method to
> use? or should i be using discriminant analysis in this case? thanks!
> --
> View this message in context:
> http://www.nabble.com/logistic-regression-tp17678490p17678490.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD
>

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Whanger, J. Mr. CTR

Re: logistic regression

Azam,

There are two additional issues to consider: (1) The proportional
distribution of cases in each of the binary categories. If the
distribution of the binary variable is not reasonably close to 50/50,
then logistic is more powerful. (2) The distribution of the predictor
variables. If they are not reasonably close to normally distributed,
then again logistic is the more powerful method. However, if the binary
variable is close to a 50/50 distribution and the predictors are
reasonably close to normally distributed, then they will provide quite
similar results.

Regards,

Jim

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
[hidden email]
Sent: Thursday, June 05, 2008 4:31 PM
To: [hidden email]
Subject: Re: logistic regression

Thanks Paul! Just wondering how many cases would you consider lots,
because some of these data sets I receive are pretty big. And in the
case where I do haev enough data to use discrimant analysis, would you
then recommend I use that instead of logistic? Thanks!

Regards,

Azam

Quoting "Swank, Paul R" <[hidden email]>:

> case? thanks!
> --
> View this message in context:
> http://www.nabble.com/logistic-regression-tp17678490p17678490.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except
> the command. To leave the list, send the command SIGNOFF SPSSX-L For a

> list of commands to manage subscriptions, send the command INFO
> REFCARD
>

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Chen Wei-2

Re: logistic regression

So Jim, in any case I did not see any advantages for using discriminant
analysis over logistic analysis. Is there any notes or articles available to
explain and compare these two methods? Thanks.

Wayne

----- Original Message -----
From: "Whanger, J. Mr. CTR" <[hidden email]>
To: <[hidden email]>
Sent: Friday, June 06, 2008 4:56 AM
Subject: Re: logistic regression

> Azam,
>
> There are two additional issues to consider: (1) The proportional
> distribution of cases in each of the binary categories. If the
> distribution of the binary variable is not reasonably close to 50/50,
> then logistic is more powerful. (2) The distribution of the predictor
> variables. If they are not reasonably close to normally distributed,
> then again logistic is the more powerful method. However, if the binary
> variable is close to a 50/50 distribution and the predictors are
> reasonably close to normally distributed, then they will provide quite
> similar results.
>
> Regards,
>
> Jim
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
> [hidden email]
> Sent: Thursday, June 05, 2008 4:31 PM
> To: [hidden email]
> Subject: Re: logistic regression
>
> Thanks Paul! Just wondering how many cases would you consider lots,
> because some of these data sets I receive are pretty big. And in the
> case where I do haev enough data to use discrimant analysis, would you
> then recommend I use that instead of logistic? Thanks!
>
>
> Regards,
>
> Azam
>
>
> Quoting "Swank, Paul R" <[hidden email]>:
>
>> If you're going to use discriminant analysis then you need a lot of
>> data. Logistic would be the best plan.
>>
>> Paul R. Swank, Ph.D.
>> Professor and Director of Research
>> Children's Learning Institute
>> University of Texas Health Science Center - Houston
>>
>>
>> -----Original Message-----
>> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf
>> Of jimjohn
>> Sent: Thursday, June 05, 2008 3:07 PM
>> To: [hidden email]
>> Subject: logistic regression
>>
>> If I have a dichotomous categorical dependent variable (ie the only
>> values it can take are 1=yes and 0=no), and my independent variables
>> are all continuous (not categorical), then is logistic regression the
>> best method to use? or should i be using discriminant analysis in this
>
>> case? thanks!
>> --
>> View this message in context:
>> http://www.nabble.com/logistic-regression-tp17678490p17678490.html
>> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>>
>> =====================
>> To manage your subscription to SPSSX-L, send a message to
>> [hidden email] (not to SPSSX-L), with no body text except
>> the command. To leave the list, send the command SIGNOFF SPSSX-L For a
>
>> list of commands to manage subscriptions, send the command INFO
>> REFCARD
>>
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
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>
> =====================
> To manage your subscription to SPSSX-L, send a message to
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> command. To leave the list, send the command
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jimjohn

Re: logistic regression

In reply to this post by Whanger, J. Mr. CTR

Thanks a lot Jim!

Azam

Quoting "Whanger, J. Mr. CTR" <[hidden email]>: