Do you have the standard deviation of errors? If you don't, you need to assume one value.

From the general equation of linear regression, with an error term e, which follows a normal distribution N(0,sigma_e):

y = mx+n + e

you can simulate y values as follows, assuming that x is in the range of (0,999):

x = runif(100, min = 0, max = 999)

y = m*x+y + rnorm(100, mean = 0, sd = sigma_e)

Saurav Vaia,

The mean is the regression equation itself. However, the std. dev. can/may be back calculated from the R-square value.

Exequiel and Nazmul already gave you the theory. Now if you want a straightforward solution, follow my example.

You have something like this:

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madeup_data