4. (a) Find the best least squares fit by a linear function to the data below. a|-1 |0|1|2 y|0|1|39 (b) Assume that the vectors in R³ have Euclidean inner product. (i) Use the Gram-Schmidt process to transform the following basis into orthogonal basis. uj = (1, 1, 1), u2 = (0, 1, 1), uz = (1, 2, 3). %3D (ii) Convert the orthogonal basis obtained in part (i) into orthonormal basis.
4. (a) Find the best least squares fit by a linear function to the data below. a|-1 |0|1|2 y|0|1|39 (b) Assume that the vectors in R³ have Euclidean inner product. (i) Use the Gram-Schmidt process to transform the following basis into orthogonal basis. uj = (1, 1, 1), u2 = (0, 1, 1), uz = (1, 2, 3). %3D (ii) Convert the orthogonal basis obtained in part (i) into orthonormal basis.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:4. (a)
Find the best least squares fit by a linear function to the data below.
a |-1|0|1|2
y 0139
(b)
Assume that the vectors in R have Euclidean inner product.
(i)
Use the Gram-Schmidt process to transform the following basis into
orthogonal basis.
uj = (1, 1, 1),
u2 = (0, 1, 1),
Uz = (1, 2, 3).
(ii)
Convert the orthogonal basis obtained in part (i) into orthonormal
basis.
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