Exercise 3-8 refer to \({{\bf{P}}_{\bf{2}}}\) with the inner product given by evaluation at \( - {\bf{1}}\), 0, and 1. (See Example 2).

5. Compute \(\left\| p \right\|\] and \[\left\| q \right\|\), for p and q in Exercise 3.

Short Answer

Expert verified

The values are \(\left\| p \right\| = \sqrt {50} \), and \(\left\| q \right\| = \sqrt {27} \).

Step by step solution

01

Write the results from Exercise 3

\(\begin{array}p\left( { - 1} \right) &= 4 - 1\\ &= 3\end{array}\)

\(\begin{array}p\left( 0 \right) &= 4 + 0\\ &= 4\end{array}\)

\(\begin{array}p\left( 1 \right) &= 4 + 1\\ &= 5\end{array}\)

And,

\(\begin{array}q\left( { - 1} \right) &= 5 - 4{\left( { - 1} \right)^2}\\ &= 5 - 4\\ &= 1\end{array}\)

\(\begin{array}q\left( 0 \right) &= 5 - 4{\left( 0 \right)^2}\\ &= 5\end{array}\)

\(\begin{array}q\left( 1 \right) &= 5 - 4{\left( 1 \right)^2}\\ &= 5 - 4\\ &= 1\end{array}\)

02

Find the value of \(\left\| p \right\|\)

The value of \[\left\| p \right\|\] can be calculated as follows:

\(\begin{array}\left\| p \right\| &= \sqrt {\left\langle {p,p} \right\rangle } \\ &= \sqrt {p\left( { - 1} \right)p\left( { - 1} \right) + p\left( 0 \right)p\left( 0 \right) + p\left( 1 \right)p\left( 1 \right)} \\ &= \sqrt {\left( 3 \right)\left( 3 \right) + \left( 4 \right)\left( 4 \right) + \left( 5 \right)\left( 5 \right)} \\ &= \sqrt {50} \end{array}\)

Thus, the value of \(\left\| p \right\|\) is \(\sqrt {50} \).

03

Find the value of \(\left\| q \right\|\)

The value of \[\left\| q \right\|\] can be calculated as follows:

\(\begin{array}\left\| q \right\| &= \sqrt {\left\langle {q,q} \right\rangle } \\ &= \sqrt {q\left( { - 1} \right)q\left( { - 1} \right) + q\left( 0 \right)q\left( 0 \right) + q\left( 1 \right)q\left( 1 \right)} \\ &= \sqrt {\left( 1 \right)\left( 1 \right) + \left( 5 \right)\left( 5 \right) + \left( 1 \right)\left( 1 \right)} \\ &= \sqrt {27} \end{array}\)

Thus, the value of \(\left\| q \right\|\) is \(\sqrt {27} \).

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Most popular questions from this chapter

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

12. \(\left( {\begin{aligned}{{}{}}1&3&5\\{ - 1}&{ - 3}&1\\0&2&3\\1&5&2\\1&5&8\end{aligned}} \right)\)

(M) Use the method in this section to produce a \(QR\) factorization of the matrix in Exercise 24.

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}4\\6\end{aligned}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{aligned}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}6\\{ - 2}\\3\end{aligned}} \right)\)

5. \(\left( {\frac{{{\mathop{\rm u}\nolimits} \cdot {\mathop{\rm v}\nolimits} }}{{{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm v}\nolimits} }}} \right){\mathop{\rm v}\nolimits} \)

Let \(\overline x = \frac{1}{n}\left( {{x_1} + \cdots + {x_n}} \right)\), and \(\overline y = \frac{1}{n}\left( {{y_1} + \cdots + {y_n}} \right)\). Show that the least-squares line for the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) must pass through \(\left( {\overline x ,\overline y } \right)\). That is, show that \(\overline x \) and \(\overline y \) satisfies the linear equation \(\overline y = {\hat \beta _0} + {\hat \beta _1}\overline x \). (Hint: Derive this equation from the vector equation \({\bf{y}} = X{\bf{\hat \beta }} + \in \). Denote the first column of \(X\) by 1. Use the fact that the residual vector \( \in \) is orthogonal to the column space of \(X\) and hence is orthogonal to 1.)

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\[W\].

8.\[y = \left[ {\begin{aligned}{ - 1}\\4\\3\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\{\bf{1}}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\{ - 2}\end{aligned}} \right]\]

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