In this assignment we will use a simple training dataset to learn the parameters of two different models. The Welch's t-test will be used in conjunction with leave-one-out cross- validation and root-mean-squared-error (RMSE) to determine whether there is statistical significance in the performance of the two models. You are asked to write code to numerically solve this problem. You are required to submit this code in Canvas along with your analysis. Consider the following training dataset. Xi 0.100 0.200 0.250 0.350 0.400 0.450 0.500 0.600 0.750 10 0.800 11 0.850 12 0.900 0.603 0.986 0.891 0.834 0.572 0.353 0.085 0.371 0.967 0.989 0.749 0.382 2 7 The models proposed to describe this data are a linear combination of basis functions with 0(x) = 1, so that wo acts as a bias. The goal is to infer model parameters w from the data. M-1 0 Two competing models are proposed to describe the data. The difference between the Two competing models is the order of the expansion M. Model Basis function Order Parameters of basis functions 5Hi $0.000, 0.333,0.667,1.000] >j(T) - exp 2s2 S 0.3 j (x) exp 0.000,0.167,0.333,0.500, 0.667, 0.833,1.000) s 0.3 2s2 In this assignment we will use a simple training dataset to learn the parameters of two different models. The Welch's t-test will be used in conjunction with leave-one-out cross- validation and root-mean-squared-error (RMSE) to determine whether there is statistical significance in the performance of the two models. You are asked to write code to numerically solve this problem. You are required to submit this code in Canvas along with your analysis. Consider the following training dataset. Xi 0.100 0.200 0.250 0.350 0.400 0.450 0.500 0.600 0.750 10 0.800 11 0.850 12 0.900 0.603 0.986 0.891 0.834 0.572 0.353 0.085 0.371 0.967 0.989 0.749 0.382 2 7 The models proposed to describe this data are a linear combination of basis functions with 0(x) = 1, so that wo acts as a bias. The goal is to infer model parameters w from the data. M-1 0 Two competing models are proposed to describe the data. The difference between the Two competing models is the order of the expansion M. Model Basis function Order Parameters of basis functions 5Hi $0.000, 0.333,0.667,1.000] >j(T) - exp 2s2 S 0.3 j (x) exp 0.000,0.167,0.333,0.500, 0.667, 0.833,1.000) s 0.3 2s2