About the weights assigned in the linear regression

I have this confusion related to linear regression. Lets say I have two predictors $x_1$ and $x_2$ and the target is $y$. I learn a linear regression with $y \sim x_1,x_1 \cdot x_2,x_2$ with $x_1 \cdot x_2$ being the interaction term. Lets suppose I get weights or parameters 1,10,2,1 for $x_1,x_1 \cdot x_2,x_2$ and the intercept term.

So can I say that my intercept term got greater weight and the predictors $x_1$ and $x_2$ have significant interaction?

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Weights are usually assigned to cases, not to parameters. Maybe what you mean is that those are your least-squares parameter estimates, rather than that those are your weights. "Significant interaction" should mean that you would reject the null hypothesis that the interaction coefficient is zero. You can't tell whether that's appropriate just by looking at the values of the estimates. You need some information about residuals. Use F-tests that emerge from the appropriate ANOVA table. You haven't given us enough information to know the result of that test. – Michael Hardy Apr 17 '13 at 14:43
Linear regression is "off-topic"? Really?!? – user1729 Apr 17 '13 at 15:40
@MichaelHardy. That's exactly what I need. Can you suggest some tests to help me with it? – user34790 Apr 17 '13 at 16:48