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How should the error term of a linear regression be weighted to ensure that the OLS method is used?
I am fitting a linear regression model and then calculating the test statistic by taking the t-statistic of the coefficient multiplied by the corresponding standard error. Is my interpretation of the t-statistic correct or is it incorrect? My logic is that the test statistic is the difference between the expected and actual mean of the sample and hence in the case of OLS there should be a bias correction (however small) from the OLS assumptions and hence dividing the test statistic by its standard error. Is my understanding correct or are there any other nuances to the t-statistic I should be aware of?