I point out that there are times when using no intercept/"constant is zero" is a very poor choice. In fact, it is rare when it is the right choice. So unless you have a good reason for using no intercept/"constant is zero", don't do it.
When I working in the SAS system the answer show looks different,I saw the coefficient intercept p value is 0.78(over 0.05) which suggests that the intercept variable is not statistically significant.
Intercept not significant is not really a valid reason to remove it from the model. You are forcing the model through the point x=0 y=0 which may not be a good thing to do in most cases — this is not about statistical significance. You cause the other parameter estimates to be biased; and the residuals will not add to zero. https://stats.stackexchange.com/a/7950
Lastly, just do a plot of the fit between the two different regressions. Here's made up data, we add a certain amount of soap to a dish of water, agitate the water, and then measure the height of the suds formed.
data suds; input soap suds; cards; 0.6 1.1 0.7 1.2 0.8 1.33 0.9 1.39 1 1.47 1.2 1.6 ; title 'Model with Intercept'; proc reg data=suds; ods select fitplot; model suds=soap; run; quit; title 'Model without Intercept'; proc reg data=suds; ods select fitplot; model suds=soap/noint; run; quit;
Here are the plots, by looking at them, which do you think fits better?
It has been a long time since I did the math, but I believe that R-squared without an intercept has to be interpreted differently than with an intercept, and so the two R-squared values are not directly comparable.
Adding: here is a discussion, which includes the math, explaining why you shouldn't compare R-squared with intercept to R-squared without intercept. https://stats.stackexchange.com/questions/26176/removal-of-statistically-significant-intercept-term-...
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