Under standard assumptions (X(nxp), y(nx1), B(px1), ɛ(nx1)) y=XB+ɛ, ɛ~(0, o^2 I_n) model and RB =r constraint are given (R(mxp), with full column rank, r(mx1)). Explain how to obtain the Constrained Least Squares estimator.
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- Suppose we are doing ordinary least-squares linear regression with a fictitious dimension. Which of thefollowing changes can never make the cost function’s value on the training data smaller? A: Discard the fictitious dimension (i.e., don’t append a 1 to every sample point). B: Append quadratic features to each sample point. C: Project the sample points onto a lower-dimensional subspace with PCA (without changing the labels) andperform regression on the projected points. D: Center the design matrix (so each feature has mean zero).1. Find the value of SSE that is minimized by the least squares method.If Q has orthonormal columns, what is the least squares solution x to Qx = b?
- Use the pseudoinverse to find a least squares solution Ax=b9) Use Lagrange multiplier to find the indicated extrema, assuming that x and y are positive.The coefficient matrix is not strictly diagonally dominant, nor can the equations be rearranged to make it so. However, both the Jacobi and the Gauss-Seidel method converge anyway. Demonstrate that this is true of the Gauss-Seidel method, starting with the zero vector as the initial approximation and obtaining a solution that is accurate to within 0.01.
- Again Find the parabola that gives the best least squares approximation to the points. Please solve this problem as fast as possible please. Do not pay attention to the selected option, I am just trying myself at linear algebra :)Find the least squares approximating line for the given points and compute the corresponding least squares error. (0, 4), (1, 1), (2, 0)Give two properties of the line estimated with the method of least squares.
- A chemical company, seeking to study the effect of extraction time on the efficiency of an extraction operation, obtained the data from a table: Fit a straight line to the data given with the least squares method and use it to predict the extraction efficiency that would be expected when the extraction time is 35 minutes. x = Extraction time (minutes)y = Extraction efficiency (%)What is the significance of R and R2 in gression model?In required problems, only Simpson's Rule (take the number of sections as n=4), NewtonRaphson Method, Least Squares Method should be used.