# In linear regression, why is the T-value the same whether or not I standardize the dependent and independent variables?

I standardized my x and y variables by subtracting by the mean and dividing by the standard error. I thought that was "fixing" my data, but I noticed the regression t-value that's produced in R is the same using either method. I'm just curious if anyone can explain the intuition behind that.

• Think of the $t$-value as a measure of deviation from the null $H_0: \beta_i = 0$ in this case. Would multiplicative or additive transformations change those? – Firebug Oct 30 '16 at 18:31