2. In class we discussed finding regression parameters by minimizing the square error (L2 norm): E ( |X) t-1 While this is the most common method, another common method is to minimize the absolute deviation (Li norm): t-1 Which method is more robust against the effect of outliers (meaning the resulting fit would be less affected by a small number of extreme, possibly anomalous, outliners)? Give a brief and intuitive explanation as to why this is 2. In class we discussed finding regression parameters by minimizing the square error (L2 norm): E ( |X) t-1 While this is the most common method, another common method is to minimize the absolute deviation (Li norm): t-1 Which method is more robust against the effect of outliers (meaning the resulting fit would be less affected by a small number of extreme, possibly anomalous, outliners)? Give a brief and intuitive explanation as to why this is