Why do I use gradient descent for a linear regression instead of differentiating for the minimum error point?

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I am wondering why we use gradient descent instead of just differentiating to find the minimum error point. From my limited mathematical knowledge, the total error is a quadratic form and we can just differentiate the minimum error point.