How do i construct a design matrix for a least square problem?

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    Design Matrix Square
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SUMMARY

To construct a design matrix for a least squares problem when fitting a polynomial of degree n-1 to a function f(t) using values at points z = [z1; z2; ...; zm], the matrix A is formed by evaluating the polynomial basis functions at the points z. The vector b is constructed from the function values f(z). The relationship between t and z must be clearly defined to ensure accurate construction of the least squares problem Ax = b.

PREREQUISITES
  • Understanding of polynomial regression and least squares fitting
  • Familiarity with matrix notation and operations
  • Knowledge of function evaluation at discrete points
  • Basic concepts of linear algebra
NEXT STEPS
  • Study polynomial basis functions for least squares fitting
  • Learn about constructing design matrices in NumPy
  • Explore the relationship between independent and dependent variables in regression
  • Investigate the use of libraries like SciPy for optimization problems
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Data scientists, statisticians, and anyone involved in regression analysis or polynomial fitting will benefit from this discussion.

papasmurfff
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Suppose we want to to fit an arbitrary function f(t) with a polynomial of
degree n - 1 using the values of the function in an arbitrary set of points z = [z1; z2; : : : ; zm].

how would do i construct the least squares problem Ax=b. in other words, how would i construct the matrix A and b in terms of f(t) and z ?
 
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...using the values of the function in an arbitrary set of points z...

Not sure what you mean here. Do you know the relationship of t to z?
 

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