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Sep 8, 2012 11:27 PM
(3184 views)

Hi all

I am new to SAS and advanced statistics and am having problems understanding the result that a logistic regression is showing me.

I am doing this work for a job interview and thus desperately need some advice promptly.

I am analysing NFL trends for qualifying to the Play Offs

I would expect a stat called Combined EPA to be heavily correlated with qualifying for the NFL Playoffs.

When Qualifying on the Y axis and Combined EPA on the X axis I get the following result:

This appears to imply that as Combined EPA increases the probability of not qualifying increases, which is the opposite of my hypothesis

However I then plotted the graph with the axes switched and got the following result

Clearly from this graph teams that qualify have on average a higher Combined EPA then teams that didnt qualify.

Given these 2 graphs could someone please explain to me the correlation in the 1st graph between Not qualifying and a higher Combined EPA?

Thank you in advance for any assistance

D Scott

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The two plots are compatible. In the first plot all "Yes" are placed above the line and All "No" below the line. The line is the predicted probability for "No". So at EPA = 0 about 70% is expected to not qualify. At 300 virtually all will qualify.

JMP uses the alphabetic order of categories by default. If you prefer the the inverse curve set the reverse order in the value ordering property for the EPA column.

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The two plots are compatible. In the first plot all "Yes" are placed above the line and All "No" below the line. The line is the predicted probability for "No". So at EPA = 0 about 70% is expected to not qualify. At 300 virtually all will qualify.

JMP uses the alphabetic order of categories by default. If you prefer the the inverse curve set the reverse order in the value ordering property for the EPA column.

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Hi MS

Thanks for the assistance, that makes perfect sense now.

I got the job in the end and im glad I can use the logistic regression tool now.

Regards

DScott

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An alternative way of looking at the curve is to use the profiler. This makes the meaning of the curve more apparent.

Dave

-Dave