Chapter 6: Q7E (page 331)
Compute the least-squares error associated with the least square solution found in Exercise 3.
Short Answer
The least-square error is \(2\sqrt 5 \).
Chapter 6: Q7E (page 331)
Compute the least-squares error associated with the least square solution found in Exercise 3.
The least-square error is \(2\sqrt 5 \).
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Get started for freeUse the Gram–Schmidt process as in Example 2 to produce an orthogonal basis for the column space of
\(A = \left( {\begin{aligned}{{}{r}}{ - 10}&{13}&7&{ - 11}\\2&1&{ - 5}&3\\{ - 6}&3&{13}&{ - 3}\\{16}&{ - 16}&{ - 2}&5\\2&1&{ - 5}&{ - 7}\end{aligned}} \right)\)
Question: In Exercises 3-6, verify that\(\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\)is an orthogonal set, and then find the orthogonal projection of y onto\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\).
4.\(y = \left[ {\begin{aligned}{\bf{6}}\\{\bf{3}}\\{ - {\bf{2}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{\bf{3}}\\{\bf{4}}\\{\bf{0}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{ - {\bf{4}}}\\{\bf{3}}\\{\bf{0}}\end{aligned}} \right]\)
In Exercises 1-4, find the equation \(y = {\beta _0} + {\beta _1}x\) of the least-square line that best fits the given data points.
Find a \(QR\) factorization of the matrix in Exercise 11.
(M) Use the method in this section to produce a \(QR\) factorization of the matrix in Exercise 24.
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