Let's say I have the following data on leads, monthly media spend, and clicks
Month Leads Media Clicks
Jan 150 1000 500
Feb 200 1000 550
March 300 1200 800
Let's say I run a linear regression where y is leads and the predictors are media and clicks. That's good, I know the relationships between these variable and can generate some lags to produce predictions. But what if I had spent 500 (or 2000 or 0) on media, what would have occurred. How do I perform this type of 'counter-factual' analysis where I attempt to find the results of a model if the actual value from one or two of the predictors was lower or higher? What is the standard approach (aka statistically proper approach)? Is it just a matter to "adjusting" the data to the 'new' number and rerunning the regression? or maybe simulating a regression 100+ times with 100+ different values for media?