linear regression statistical parameters

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Reza S
Reza S 2015-4-20
评论： Reza S 2015-4-29
Hello,
This is a question following my previous one but I explain the problem here as well. I am trying to use linear and nonlinear regression to predict a response. I am wondering how I can get the most possible statistical results from regress or nlinfit (like durbin watson, probabilities, R2, adjusted R2, etc.).
y=(c.^4+2*c.*p+3*p.^3-c+2*d.^0.5)'; % a sample response
X = [c;p;d]';
beta0 = [1 -2 0 -1 0 1 1];
X = [ones(size(c)); c.^4 ;c.*p; p.^3 ;c; d.^0.5]';
[b,stats] = regress(y,X)
Results: b =
0
1.0000
2.0000
3.0000
-1.0000
2.0000
stats = (how to interpret these?)
0 0
NaN NaN
NaN NaN
NaN NaN
NaN NaN
NaN NaN

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回答（1 个）

Ahmet Cecen
Ahmet Cecen 2015-4-20
There is a good chance there are other things wrong with your problem, but first off, it is:
[b,bint,r,rint,stats] = regress(y,X)
not:
[b,stats] = regress(y,X)
in your case, your stats is actually bint...
if you do not want the other results, do this instead:
[b,~,~,~,stats] = regress(y,X)
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Ahmet Cecen
Ahmet Cecen 2015-4-21
编辑：Ahmet Cecen 2015-4-21
Now your stats looks like its actually rint. Stats would look like:
stats=
number <- R2 statistic
number <- the F statistic
number <- p value of F statistic
number <- estimate of the error variance

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