Should I use multiple t-tests?

I want to do a statistical test to find out the effect of 3 binary ("yes/no") variables on one continuous variable.





For example, is there a statistical difference in "happiness score, from 1 to 100" between people in the following groups?





Sex M/F


Obese Y/N


Computer-user Y/N





What would be the best statistical test to use?


I thought that 3 t-tests (for effect of Sex, Obesity and then Computer-use) would work.





Would it be easier if there were only 2 yes/no variables (Obesity and use of computer)?





If I use multiple t-tests, do I need a Bonferroni correction (and can someone explain what that really means? I read it in a book).|||This is probably the most widely used statistical test of all time, and certainly the most widely known. It is simple, straightforward, easy to use, and adaptable to a broad range of situations.





Its utility is occasioned by the fact that scientific research very often examines the phenomena of nature two variables at a time, with an eye toward answering the basic question: Are these two variables related? If we alter the level of one, will we thereby alter the level of the other? Or alternatively: If we examine two different levels of one variable, will we find them to be associated with different levels of the other?