Existence and Uniqueness of a Linear Least Squares Solution

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SUMMARY

The discussion focuses on the existence and uniqueness of solutions for the linear least squares problem, specifically the equation y ≈ xB, where the goal is to minimize the error ||y - xB||. The key equations involved are x' x B = x' y, which establishes the relationship between the variables. The solution involves deriving the residuals r_i = y_i - (B_0 + B_1 x_i) and setting the derivatives with respect to the parameters to zero to find optimal values for B_0 and B_1.

PREREQUISITES
  • Understanding of linear algebra concepts, particularly matrix operations.
  • Familiarity with the least squares method and its applications.
  • Knowledge of calculus, specifically differentiation and optimization techniques.
  • Basic understanding of numerical analysis principles.
NEXT STEPS
  • Study the derivation of the normal equations in linear least squares.
  • Learn about the geometric interpretation of least squares solutions.
  • Explore the use of software tools like MATLAB or Python's NumPy for implementing least squares regression.
  • Investigate the conditions for the existence and uniqueness of solutions in linear systems.
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Students in numerical analysis, data scientists, and anyone involved in statistical modeling or regression analysis who seeks to understand the foundations of linear least squares solutions.

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I'm studying for my numerical analysis final on tuesday, and I know this is going to be one of the problems, so any help is greatly appreciated.

Homework Statement


State and prove existence and uniqueness for the solution of the linear least squares problem.

Homework Equations


y \approx x B
x' x B = x' y

The Attempt at a Solution


linear least squares finds B such that \| y - x B \| is minimized.

Since this is linear least squares, y = B_0 + B_1 x

r_i = y_i - (B_0 + B_1 x_i)
For 1 \le i \le n, \delta r_i / \delta x_i = 0
Then (\delta y_i / \delta x_i) - B_1 = 0

I missed this lecture and I can't find much help online, so I could be headed in the wrong direction. Thanks!
 
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so say you want to find a,b such that it minimises the least square error in y = a + bx

start with the sum of the squares, to characterise the error for given a and b, then minimise w.r.t. a,b and you should be most of the way there
 

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