How to modify a coefficient in a linear regression I think what I need may be called reverse regression. Usually, linear regression is to lm(y~x1+..x2) so that to find the estimated coefficients of each variable. Then we can write the formula which is y = constant + coef1 * x1 +coef2*x2 +...+coefn*xn. But I think the reverse way is that we modify the some values of coef*variable and get the coef.
The reverse regression is that we know the values of y and values of something like coef1*x1, however, we
The way I know is to modify the coefficients of x1 or x2. Then, try lm((y - new_coef*x1) ~ x2 + x3 to double check the coefficient of x1.
Are there any other ways ?
The example above is made up, so there is no true dataset. I am just curious if there is any way to modify coefficients

Processes that reduce the magnitude of coefficients are called "regularization". Normally, though, you decide what regularization to do before performing the regression, rather than looking at your regression first, and then tailoring the regularization to a particular goal for a coefficient.
Regularization also usually is applied equally to all the coefficients, but it doesn't have to be (and if you don't standardize your variables, then your choice of units can make a nominatively symmetric regularization have an asymmetric effect). If you have reason to think that one of your variables is less likely to be significant, it is valid to use a different regularization hyperparameter for that variable.
Regularization is equivalent to a Bayesian prior, the solution mkt brought up, but you'll probably get more useful results looking up how to do regularization than Bayesian priors.
