Good or bad is subjective

When you look at a model a good place to start is the F test and the significance. If it is below .05 than by common usage it is statistically significant. Then you decide if substantively the adjusted R squared is high enough to make the model seem useful. This depends on what you are analyzing, does explaining 51 percent of the total variance in your dependent variable make your analysis useful or not?

The b's are the X variables (2 and 3 I assume). They tell you the change in Y for a one unit change in that X. Again you have to decide if that is meaningful. The p value tells you if it is statistically signficant - much like the F test for the model.

I do not understand question 3. The p values tell you if a variable has statistically significant effect, this effect is the slope which your ouput calls coefficients.

It is not a good idea to use regression p values, which are associated with t, if the distribution is not normal although to some extent the results are robust to nonnormality especially if the sample size is large.

We don't solve problems here, we make suggestions (particularly if it is homework related).