What is the difference between multiple regression & mutivariate regression? I have data on GDP growth as a dependent variable and growth in main production sectors of Pakistan such as mining, electricity, communication, manufacturing and electricity. I am supposed to run a regression on it. Is there any difference between multiple regression and multivariate regression? If so, than what is it?  Which is suitable for my data?
 A: "Multiple regression" refers to situations in which you have more than one predictor / explanatory variable ($X$).  
"Multivariate regression" refers to situations in which you have more than one response / outcome / dependent variable ($Y$).  
It is also possible to have both multiple predictors and multiple responses, in which case you could call it a "multivariate multiple regression".  But since people rarely have only one predictor, I don't think people are worried about making the multiple predictor part distinct.  This raises the question of why we worry about "multiple" vs. "simple" (only one predictor) regression in the typical case when you have only one response.  I think that it is mostly for historical and pedagogical (teaching) reasons: simple regression was worked out first, and is taught first to help students get the main ideas before going further.  
In your case, I gather you have only one response variable (Pakistan's GDP growth), and several predictor variables (growth in mining, electricity, communication, manufacturing and electricity), so your regression model will be a regular old multiple regression.  
