Hi venkat,
1. I input into this XLS, I get p value of about 4.3%
see: http://sheet.zoho.com/public/btzoho/0924-fdist
(I don’t know how to inuit; without the F lookup, this would be a hard question, IMO)
2. I think it helps to keep in mind that OLS regression is a method to produce estimates (e.g., slope, intercept) from estimators (the “recipe” formulas). It’s the common (best?) method because of the desirable *properties* of those estimators, if the assumptions are valid. As usual (e.g. CAPM), some assumptions can typically be bent while other will break the model. Gujarati is definitely in the camp that heteroskedasticy is not necessarily deadly; but technially, the violation of the assumption has implication on estimators. Spefically, from Gujarati:
“Heteroscedasticity does not destroy the unbiasedness and consistency properties of OLS estimators. But these estimators are no longer minimum variance or efficient. That is, they are not BLUE.”
I am unsure why the term “unconditional” is used (I have not given this deep thought, so maybe i miss something) but i *think* homoskedasticity implies equivalently:
E(error^2) = constant
E(error^2 | explanatory variable) = constant
...so i think the unconditional is unnecessary ...
But in any case, I answerd this way to draw your attention to the properties of the estimators. On closer look, we are a long way from saying a regression works or does not; e.g., OLS is only one approach, within OLS, we can produce estimates that are more/less useful
3. This is a classic practical consequence of multicollinearity (see Gujurati chapter 12 for detail disucssion)
David
Practice qn on multiple regression 
