Suitable data for regression I am new to the wonderland called Statistics. I studied a lot (finished Greene) about linear regression but I never got this clear in my head: if I am given data and asked to run a regression, what tests should I run to see whether data is appropriate to run a good regression? How do I know my data is good enough to give me a good model?
For example, we were asked to perform some data analysis on a survey our entire class conducted in Delhi. I didn't believe in the sampling strategies employed by other groups, so I was hesitant in performing a regression analysis. So, what should I do? 
 A: If you are given data and asked to run a regression, one question you should seek to answer is When Regression is not applicable? Without knowing your data and the type of the linear model you have been asked to fit it is only possible to get you started in this direction by providing some general advice. It may begin as follows:
There are many cases where you won't get useful results from regression. The two most common kinds of issues are (1) when your data contain major violations of regression assumptions and (2) when you don't have enough data (or of the right kinds). Core assumptions behind regression include 


*

*That there is in fact a relationship between the outcome variable and the predictor variables.

*That observations are independent. 

*That the residuals are normally distributed and independent of the values of variables in the model. 

*That each predictor variable is not a linear combination of any others and is not extremely correlated with any others. 

*Additional assumptions depend on the nature of your dependent variable; for example ...


If this is what you are after, then please read more here. Of course, the time will come when you stop calling yourself a stats newbie, and will rightly disagree with some of the above, but those will be different questions or even answers.   
