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but start working on the concept of omitted variable bias and letamp;#39;s do it based on an example so that we donamp;#39;t focus too much on all the notations with expected values and all that but actually intuition and thatamp;#39;s gonna make all the sense because what matters in this whole statistic / econometrics thing is to be able to read tables is to be able to interpret things so thatamp;#39;s what weamp;#39;re going to focus on now notice this we have here two models in the first model weamp;#39;re regressing earnings right how much people make in money based on their height apparently thatamp;#39;s a determinant itamp;#39;s docHub as we can see and that effect is given to us by the slope coefficient which is 700 7.67 letamp;#39;s suppose that height is measured in centimeters earnings is measured in dollars so how would we would interpret it is if someone is has a height hired by one centimeter so height increases by one centimeter then the then the earnings w
