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11-25-2015 08:28 AM

Dear All,

I have 2 regression (linear) models and i am comparing the results of those.in both the cases independent variable is weight and the dependent var is price

MODEL 1:

The slope for weight in model 1 is 1.392 with a significance value of .000 & standard error of .009

MODEL 2:

The slope for weight in model 1 is 1.375 with a significance value of .000 & standard error of .008

From the above output in both the models, weight is a good predictor var at alpha of 0.5 however as there is a very slight variance in both the value so does this signify anything. Please note that in first model there is no other predictor variable whereas in model 2 i have another 2 variables.

With the inclusion of additional var (IV) it should have impacted my R-square not the slope value. Kindly suggest.

Thanks, Shivi

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Solution

11-26-2015
12:28 AM

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11-25-2015 08:53 AM

Including additional independent variables can change the coefficient of weight, unless the additional independent variables are orthogonal to weight (unlikely, unless it is a designed experiment).

I suppose you could still do a statistical comparison of the slopes, but I think that's sort of meaningless here, you know the slopes will be different because of the additional two independent variables.

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Solution

11-26-2015
12:28 AM

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11-25-2015 08:53 AM

Including additional independent variables can change the coefficient of weight, unless the additional independent variables are orthogonal to weight (unlikely, unless it is a designed experiment).

I suppose you could still do a statistical comparison of the slopes, but I think that's sort of meaningless here, you know the slopes will be different because of the additional two independent variables.

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11-25-2015 12:05 PM

You should look at the adjusted R square as well, as it attempts to account for the number of variables in your model.

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11-25-2015 12:12 PM - edited 11-25-2015 12:14 PM

Shivi,

Are you using Enterprise Miner?

What is the ASE (average squared error) or what does the distribution of the predicted price looks like for each model? Fit stats are really important but actually looking at the predicted dependent variable helps assess if you need extra work (transformations, other methods, ensemble methods, etc).

Also, why do you only have one independent variable? This is the big data world and you and SAS can handle more. Really curious here....

[Edit: Scratch this question. I just noticed you mentioned you had other 2 variables]

Thanks!

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11-26-2015 12:30 AM

Hi,

I was running stepwise regression so in first step the model had 1 significant variable. Thereafter further significant variables were added.

Regards, Shivi

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11-25-2015 01:11 PM

The simplest way to test the slopes equality is to join the datasets with

```
proc sql;
create table both as
select 1 as set, *, 0 as var1, 0 as var2 from set1
union all
select 2 as set, * from set2;
quit;
```

and test the equality of the slopes with

```
proc glm data=both;
class set;
model price = weight var1 var2 weight*set / solution;
run;
```

the significance of *weight*set* gives you the probability that the slopes are the same.

(untested)

PG

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11-26-2015 12:35 AM

Thank you for the solution i will for sure give it a try and in case of required i would seek your guidance.

Regards, Shivi

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11-26-2015 11:26 AM

It had not occured to me that set1 and set2 could be the same data. If so, then the slope comparison is pointless, as @PaigeMiller said. If you are comparing different models on the same data, it is the significance of var1 and var2 that matters, not the change in slopes. My proposed analysis is for comparing the slopes on two independent data sets, one in which has two extra variables.

PG

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11-27-2015 08:18 AM

Perhaps I'm belaboring the point, however, let's consider this situation

Data set 1: fit model Y=b0 + b1X1 + e

Data set 2: fit model Y=b0 + b1X1 + b2X2 + b3X3 + e

I would say it still doesn't make any sense to compare b1 from data set 1 to b1 from data set 2. Yes, you probably can get SAS to perform this comparison, but what does it mean? I'd say it means nothing, the fact that there are different models may affect the slope b1 and you can't assume that the different b1 values across the two different models are different just because of random variability in the data sets.

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11-26-2015 07:01 AM

My problem with doing a significance test of the coefficients to determine if they are different is taht the significance test looks for a change in slope over and above what we would expect from random variability in the system; but this change in slope could have nothing to do with random variation, it could be due to the addition of two variables into the model. I prefer the extra-sum-of-squares test to see if the fit of the MODEL (not this particular slope) is really significantly different.

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11-26-2015 11:06 AM

Continuing on the above point ...

are you comparing the slopes from two different models fit to the EXACT SAME data set? That's not the purpose of significance testing, significance testing would compare a slope computed from data set A to a slope computed from data set B. If this really is comparing two different slope estimates from two different models fit to the EXACT SAME data set, you are doing something that has no meaning, and hence the results are meaningless.