<p>In regression, variables are rarely <strong>independent</strong>. If you have a balanced experiment design, they can be made independent and uncorrelated (assuming you have no missing data). In pretty much every other situation, including every observational study (and that's what people usually come to this site with), the regressors are <strong>correlated</strong>, and hence cannot be said to be independent. While I am totally fine referring to the response variable as the "<em>dependent variable</em>", I deliberately avoid the parallel, and always refer to the regressors as "<em>explanatory variables</em>".</p>

<p>If we can help the utterly confused readers of the site, we should do this. I am in no position to enforce my idiosyncratic terminology use on everybody, but I would suggest rethinking what the <code>independent variables</code> may refer to.</p>

<p>Oh yes, the factors that come out of principal component analysis are uncorrelated (and hence independent if they are normal). And there's the method of independent component analysis, <a href="https://stats.stackexchange.com/questions/tagged/ica">properly tagged</a> <code>ica</code>.</p>
