Consider the dataset below containing information about N students, including their Assignment and Exam marks along with corresponding results. The result is the target variable in the dataset. Assume you are running a regression model to predict the result, and the prediction formula is given as: predicted_result = (0.83 * actual_result) + 15. Calculate the predicted value for all the students using this formula and subsequently compute the error using the given formula: Error = 1/N _(t=1)^N[predictedresult_t-actualresult_t]^2 Based on the calculated error, assess the performance of our regression model. Do you consider the model's performance to be good or bad? If you were to encounter the same level of error in general for other datasets, would it indicate the model's ability to predict data correctly across Assignment Marks | Exam Marks | Results 1 2 45 2 6 65 3 3 71 3 1 40 4 3 76 4 4 81 7 1 69 5 4 89 6 2 59 Figure 1: Dataset various cases? Justify your response. (Note: There is no absolute right or wrong answer; provide a reasoned explanation for your stance.)