bias(error) should not be in linear_regression fn #338
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Having + b in the calculation biases the results. It give you a regression like y = W*x + W2*b + e where e~N(0,1). You effectively add the error term into the regression. The result looks a bit like having a hidden constant term in the model. Using regular linear regression, the coefficient estimate (W) is 0.36, and removing +b, you get exactly the same results with this tf version.
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Having + b in the calculation biases the results. It give you a regression like y = Wx + W2b + e where e~N(0,1). You effectively add the error term into the regression. The result looks a bit like having a hidden constant term in the model. Using regular linear regression, the coefficient estimate (W) is 0.36, and removing +b, you get exactly the same results with this tf version.