This question is more of theoretical. I am not sure if this is the right place, but still giving it a try.
I have two variables — direct cost and indirect cost. When sales persons go for a sales pitch to a customer they know about direct cost that they are going to incur for this service, but they don't know much about indirect cost (they will come to know about it in latter stages). An estimate of indirect cost at this stage will be valuable for sales persons.
I am trying to predict indirect cost as a function of direct cost. I am doing this via a simple linear regression. I plotted scatter plot between direct cost and indirect cost and see a good linear relationship between them. I also see that direct cost and indirect cost are highly corelated to each other with correlation coefficient as 0.98, so I expected a very good prediction accuracy. But surprisingly, my prediction accuracy is not so good. I have around 200,000 points in my training data and average prediction error on training data is 17 %. Though adjusted R-Square value is 0.97. I am using
lm() function from R.
My question is that in case of simple linear regression, in general, should we expect better prediction accuracy if dependent and independent variables are highly correlated or is it my misconception? If we expect good accuracy, am I missing something here. Please note that I have also tried centering these variables around mean.