# Statistical modeling with two predictor variables that are dependent, and a response variable that is dependent on both of the predictor variables?

So I am doing a mathematical project about tennis serve speed. The question I want to dig into is that whether player's height and weight has a correlation with average first serve speed. So the predictor variables are height, and weight, and the response variable is average first serve speed. I tried using multiple linear regression, but it did not turn out well because I do not think height and weight are completely independent. In fact, I found a r value of 0.64 with my data. So is there a way for modeling the three variables together then?

Thanks

It does not matter for the linear regression that your variables are correlated, as long as they are not perfectly dependent (which they aren't).

I think that linear regression in this case is a good choice, and if weight and height indeed contribute to the first serve speed, you will get a nice, easy to communicate quantitative dependency (for example, given that the weight is constant, each 1 cm adds 1+-0.1 m/s).

More to the point, if the $R^2$ (you said $R$ but I suspect you mean $R^2$) is .64 that isn't too bad on its own. I am actually surprised that the explanatory power is that good.
There is a big difference between having a statistically significant set of coefficients (i.e., they are close the the 'actual' number) and having a high $R^2$ (explaining 64% of what determines the speed).