Use Calculus to analytically derive the expression for single variable linear regression fitting parameters using the...
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Use Calculus to analytically derive the expression for single variable linear regression fitting parameters using the sum of square error as the loss function (show your work). That is, do the mathematical optimization problem by hand rather than with a computer Model: 1 y = M(x|p) = mx + b P = (Po, P₁) = (m, b) N Loss surface: L(p) = L(m, b) = Σ (ŷ¡ – M(ŵ¡, m, b))² i=1 Use matrix calculus to analytically derive the expression for two variable linear regression fitting parameters using the sum of square error as the loss function ● ● Show your work using matrix notation From your solution infer the generalized solution for an arbitrary number of variables Use Calculus to analytically derive the expression for single variable linear regression fitting parameters using the sum of square error as the loss function (show your work). That is, do the mathematical optimization problem by hand rather than with a computer Model: 1 y = M(x|p) = mx + b P = (Po, P₁) = (m, b) N Loss surface: L(p) = L(m, b) = Σ (ŷ¡ – M(ŵ¡, m, b))² i=1 Use matrix calculus to analytically derive the expression for two variable linear regression fitting parameters using the sum of square error as the loss function ● ● Show your work using matrix notation From your solution infer the generalized solution for an arbitrary number of variables
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C The single variable linear regression fitting parameters can be derived by minimizing the sum of s... View the full answer
Related Book For
Applied Regression Analysis and Other Multivariable Methods
ISBN: 978-1285051086
5th edition
Authors: David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg
Posted Date:
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