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manonlyn
Obsidian | Level 7

I am running Proc Logistic in SAS with Stepwise selection. My results show the Intercept with an estimate of -3290.3 while the other parameters vary with values such as 0.5337, 3.11E-6, 0.000088 etc. 

 

This is the first time I've had such differing values, would anyone be able to explain in layman's terms what this means? And possibly what fixes I could do? Or maybe more things to look into? 

 

Unfortunately, I am the only analyst within the business so I have no support from others who work from me as they don't understand the methodology. 

 

Thanks in advance!

14 REPLIES 14
PaigeMiller
Diamond | Level 26

Intercept is where the regression line crosses the y-axis when all the x-variables are set to zero. The other numbers are slopes of the individual variables.

 

I don't know what there is to fix here, it's not clear that anything needs to be fixed, it's not clear that anything is wrong.

 

Have you checked for outliers in the x-variables? .

--
Paige Miller
manonlyn
Obsidian | Level 7

Sorry it's just in the past my Intercept value has been more similar to the parameter estimates, this is the first time they've been extremely different and I was wondering if I needed to fix something. 

 

Are the outliers outputted as part of Proc Logistic?

 

It might be worth noting that I'm just building quick scorecard for Segmentation Analysis before I start the actual scorecard builds. 

PaigeMiller
Diamond | Level 26

@manonlyn wrote:

Sorry it's just in the past my Intercept value has been more similar to the parameter estimates, this is the first time they've been extremely different and I was wondering if I needed to fix something. 

 

Are the outliers outputted as part of Proc Logistic?

 

It might be worth noting that I'm just building quick scorecard for Segmentation Analysis before I start the actual scorecard builds. 


You can check outliers in the x-variables via a histogram or PROC UNIVARIATE, or just about any other plotting method. There are also a number of "goodness of fit" statistics computed by PROC LOGISTIC, such as the C statistic (area under the curve), percent concordance and Somer's D.

--
Paige Miller
manonlyn
Obsidian | Level 7

Thank you I'll start with those, this has been really helpful thanks again.

Reeza
Super User

Sorry it's just in the past my Intercept value has been more similar to the parameter estimates, this is the first time they've been extremely different and I was wondering if I needed to fix something. 

 

 

That may have been coincidental. I'm not aware of any rules or logic that would indicate that should happen. 

Ksharp
Super User

I didn't see any problem in your output, so what is your problem .

For those variables have very small coefficient (3.11E-6, 0.000088 etc) , you can ignore/remove them due to  not significant .

PaigeMiller
Diamond | Level 26

@Ksharp wrote:

I didn't see any problem in your output, so what is your problem .

For those variables have very small coefficient (3.11E-6, 0.000088 etc) , you can ignore/remove them due to  not significant .


But they could be statistically significant, the statistical significance of these coefficients wasn't mentioned.

--
Paige Miller
Ksharp
Super User

I guess these P values must be >0.05 . And hope OP post these P values and confirm my guess . Smiley Happy

manonlyn
Obsidian | Level 7

The P value for the variable with the estimate of 3.11E-6 is <.0001

 
Ksharp
Super User

Can you post the whole  parameter estimator table ?

manonlyn
Obsidian | Level 7
Analysis of Maximum Likelihood Estimates
Parameter DFEstimateStandard ErrorWald Chi-SquarePr > ChiSq
Intercept 1-3290.3703.321.8867<.0001
Var1 1-0.001630.00022652.2701<.0001
Var2 1-0.005260.000466127.441<.0001
Var3 10.0009580.0002712.6080.0004
Var4 1-0.00010.0000387.19780.0073
Var5 1-0.39360.0369113.85<.0001
Var6 10.0000880.00001631.0853<.0001
Var7 1-0.0020.00029845.2192<.0001
Var8 10.01260.0031715.871<.0001
Var9 1-0.000460.0001529.28760.0023
Var10 1-0.003070.00065422.0835<.0001
Var11Y1-0.41350.096118.5191<.0001
Var12 13.11E-067.86E-0715.6228<.0001
Var13 10.0001630.00003521.8936<.0001
Var14HI1-0.2760.09887.80360.0052
Var14LO10.07720.07141.16890.2796
Var14VH1-0.48610.089129.767<.0001
Var14VL10.53370.143213.89230.0002
Var15 10.01930.0041421.7022<.0001
Var16 10.001490.00029126.3576<.0001
Var17 1-0.28490.055726.1488<.0001
Var18 10.0000880.00001823.3451<.0001
Var19 10.002910.00062821.5079<.0001
Reeza
Super User
It's often considered good practice to standardize your variables in some manner otherwise larger variables can seem more relevant just by the size. Did you standardize your variables ahead of time? Otherwise parameter estimates will just reflect the estimates.
manonlyn
Obsidian | Level 7

At the moment I'm building quick models to analyse if it's worth segmenting. I'm not sure how I would standardize variables? What does this usually include? Thank you.

PaigeMiller
Diamond | Level 26

@manonlyn wrote:

At the moment I'm building quick models to analyse if it's worth segmenting. I'm not sure how I would standardize variables? What does this usually include? Thank you.


Even though @Reeza says it is good practice to standardize the variables, I don't think standardizing is required in any way, and your models are fine without standardizing.

 

But the answer about should you standardize really depends on what "building quick models to analyse if it's worth segmenting" means, and I don't know what that means, and I don't know how you intend to do the analysis to determine if it's worth segmenting. Please explain further.

--
Paige Miller

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