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Compute the least-squares error associated with the least square solution found in Exercise 3.

Short Answer

Expert verified

The least-square error is \(2\sqrt 5 \).

Step by step solution

01

Write the results of Exercise 3

From Exercise 3, the normal equation is:

\(\left( {\begin{aligned}{{}{}}6&6\\6&{42}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = \left( {\begin{aligned}{{}{}}6\\{ - 6}\end{aligned}} \right)\)

The \({\bf{\hat x}}\) component is\(\left( {\begin{aligned}{{}{}}{\frac{4}{3}}\\{ - \frac{1}{3}}\end{aligned}} \right)\).

02

Find the matrix \(A{\bf{\hat x}} - {\bf{b}}\)

\(\begin{aligned}{}A{\bf{\hat x}} - {\bf{b}} &= \left( {\begin{aligned}{{}{}}1&{ - 2}\\{ - 1}&2\\0&3\\2&5\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{\frac{4}{3}}\\{ - \frac{1}{3}}\end{aligned}} \right) - \left( {\begin{aligned}{{}{}}3\\1\\{ - 4}\\2\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}{}}2\\{ - 2}\\{ - 1}\\1\end{aligned}} \right) - \left( {\begin{aligned}{{}{}}3\\1\\{ - 4}\\2\end{aligned}} \right)\\ &= \left( {\begin{aligned}{{}{}}{ - 1}\\{ - 3}\\3\\{ - 1}\end{aligned}} \right)\end{aligned}\)

03

Find the magnitude of  \(\left| {A{\bf{\hat x}} - {\bf{b}}} \right|\)

The value of \(\left| {A{\bf{\hat x}} - {\bf{b}}} \right|\) can be calculated as:

\(\begin{aligned}{}\left| {A{\bf{\hat x}} - {\bf{b}}} \right| &= \sqrt {{{\left( 1 \right)}^2} + {{\left( 3 \right)}^2} + {{\left( { - 3} \right)}^2} + {{\left( 1 \right)}^2}} \\ &= \sqrt {1 + 9 + 9 + 1} \\ &= \sqrt {20} \\ &= 2\sqrt 5 \end{aligned}\)

Thus, the least square error is \(2\sqrt 5 \).

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Most popular questions from this chapter

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.

  1. \(\left( {0,1} \right),\left( {1,1} \right),\left( {2,2} \right),\left( {3,2} \right)\)

[M] Let \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) be the fourth-order and fifth order Fourier approximations in \(C\left[ {{\bf{0}},{\bf{2}}\pi } \right]\) to the square wave function in Exercise 10. Produce separate graphs of \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) on the interval \(\left[ {{\bf{0}},{\bf{2}}\pi } \right]\), and produce graph of \({f_{\bf{5}}}\) on \(\left[ { - {\bf{2}}\pi ,{\bf{2}}\pi } \right]\).

In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

10.\[y = \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}1\\0\\1\\1\end{aligned}} \right]\],\[{{\bf{u}}_3} = \left[ {\begin{aligned}0\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

A healthy child’s systolic blood pressure (in millimetres of mercury) and weight (in pounds) are approximately related by the equation

\({\beta _0} + {\beta _1}\ln w = p\)

Use the following experimental data to estimate the systolic blood pressure of healthy child weighing 100 pounds.

\(\begin{array} w&\\ & {44}&{61}&{81}&{113}&{131} \\ \hline {\ln w}&\\vline & {3.78}&{4.11}&{4.39}&{4.73}&{4.88} \\ \hline p&\\vline & {91}&{98}&{103}&{110}&{112} \end{array}\)

Show that if \(U\) is an orthogonal matrix, then any real eigenvalue of \(U\) must be \( \pm 1\).

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