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Let \({\mathbb{R}^{\bf{2}}}\) have the inner product of Example 1, and let \({\bf{x}} = \left( {{\bf{1}},{\bf{1}}} \right)\) and \({\bf{y}} = \left( {{\bf{5}}, - {\bf{1}}} \right)\).

a. Find\(\left\| {\bf{x}} \right\|\),\(\left\| {\bf{y}} \right\|\), and\({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^{\bf{2}}}\).

b. Describe all vectors\(\left( {{z_{\bf{1}}},{z_{\bf{2}}}} \right)\), that are orthogonal to y.

Short Answer

Expert verified

(a) \(\left\| {\bf{x}} \right\| = 3\),\(\left\| {\bf{y}} \right\| = \sqrt {105} \)and \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2} = 225\)

(b) The set of vectors \(\left( {{z_1},{z_2}} \right)\) are in the form of \(\frac{{{z_1}}}{1} = \frac{{{z_2}}}{4}\).

Step by step solution

01

Find answer for part (a)

Find the value of \(\left\| {\bf{x}} \right\|\).

\(\begin{aligned}\left\| {\bf{x}} \right\| &= \sqrt {\left\langle {{\bf{x}},{\bf{x}}} \right\rangle } \\ &= \sqrt {\left\langle {\left( {1,1} \right),\left( {1,1} \right)} \right\rangle } \\ &= \sqrt {4\left( 1 \right)\left( 1 \right) + 5\left( 1 \right)\left( 1 \right)} \\ &= \sqrt 9 \\ &= 3\end{aligned}\)

Thus, the value of \(\left\| {\bf{x}} \right\|\) is 3.

Find the value of \(\left\| {\bf{y}} \right\|\).

\(\begin{aligned}\left\| {\bf{y}} \right\| &= \sqrt {\left\langle {{\bf{y}},{\bf{y}}} \right\rangle } \\ &= \sqrt {\left\langle {\left( {5, - 1} \right),\left( {5, - 1} \right)} \right\rangle } \\ &= \sqrt {4\left( 5 \right)\left( 5 \right) + 5\left( { - 1} \right)\left( { - 1} \right)} \\ &= \sqrt {105} \end{aligned}\)

Thus, the value of \(\left\| {\bf{y}} \right\|\) is \(\sqrt {105} \).

Find the value of \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2}\).

\(\begin{aligned}{\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2} &= {\left| {\left\langle {\left( {1,1} \right),\left( {5, - 1} \right)} \right\rangle } \right|^2}\\ &= {\left| {4\left( 1 \right)\left( 5 \right) + 5\left( 1 \right)\left( { - 1} \right)} \right|^2}\\ &= {\left| {15} \right|^2}\\ &= 225\end{aligned}\)

Thus, the value of \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2}\) is 225.

02

Find answer for part (b)

The vectors \(\left( {{z_1},{z_2}} \right)\) are orthogonal to \(y\), if and only if \(\left\langle {z,{\bf{y}}} \right\rangle = 0\). Therefore,

\(\begin{aligned}\left\langle {z,{\bf{y}}} \right\rangle &= 0\\\left\langle {\left( {{z_1},{z_2}} \right),\left( {5, - 1} \right)} \right\rangle &= 0\\4\left( {{z_1}} \right)\left( 5 \right) + 5\left( { - 1} \right)\left( {{z_2}} \right) &= 0\\20{z_1} &= 5{z_2}\\\frac{{{z_1}}}{1} &= \frac{{{z_2}}}{4}\end{aligned}\)

Thus, the set of vectors \(\left( {{z_1},{z_2}} \right)\) are in the form of \(\frac{{{z_1}}}{1} = \frac{{{z_2}}}{4}\).

So, all the multiples of the vector \(\left( {\begin{aligned}1\\4\end{aligned}} \right)\) are orthogonal to the vector y.

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Most popular questions from this chapter

Given data for a least-squares problem, \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\), the following abbreviations are helpful:

\(\begin{aligned}{l}\sum x = \sum\nolimits_{i = 1}^n {{x_i}} ,{\rm{ }}\sum {{x^2}} = \sum\nolimits_{i = 1}^n {x_i^2} ,\\\sum y = \sum\nolimits_{i = 1}^n {{y_i}} ,{\rm{ }}\sum {xy} = \sum\nolimits_{i = 1}^n {{x_i}{y_i}} \end{aligned}\)

The normal equations for a least-squares line \(y = {\hat \beta _0} + {\hat \beta _1}x\) may be written in the form

\(\begin{aligned}{c}{{\hat \beta }_0} + {{\hat \beta }_1}\sum x = \sum y \\{{\hat \beta }_0}\sum x + {{\hat \beta }_1}\sum {{x^2}} = \sum {xy} {\rm{ (7)}}\end{aligned}\)

Derive the normal equations (7) from the matrix form given in this section.

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\[W\].

9.\[y = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\]

Exercises 19 and 20 involve a design matrix \(X\) with two or more columns and a least-squares solution \(\hat \beta \) of \({\bf{y}} = X\beta \). Consider the following numbers.

(i) \({\left\| {X\hat \beta } \right\|^2}\)—the sum of the squares of the “regression term.” Denote this number by \(SS\left( R \right)\).

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)—the sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)—the “total” sum of the squares of the -values. Denote this number by \(SS\left( T \right)\).

Every statistics text that discusses regression and the linear model \(y = X\beta + \in \) introduces these numbers, though terminology and notation vary somewhat. To simplify matters, assume that the mean of the -values is zero. In this case, \(SS\left( T \right)\) is proportional to what is called the variance of the set of \(y\)-values.

20. Show that \({\left\| {X\hat \beta } \right\|^2} = {\hat \beta ^T}{X^T}{\bf{y}}\). (Hint: Rewrite the left side and use the fact that \(\hat \beta \) satisfies the normal equations.) This formula for is used in statistics. From this and from Exercise 19, obtain the standard formula for \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

In Exercises 17 and 18, all vectors and subspaces are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

a. If \(W = {\rm{span}}\left\{ {{x_1},{x_2},{x_3}} \right\}\) with \({x_1},{x_2},{x_3}\) linearly independent,

and if \(\left\{ {{v_1},{v_2},{v_3}} \right\}\) is an orthogonal set in \(W\) , then \(\left\{ {{v_1},{v_2},{v_3}} \right\}\) is a basis for \(W\) .

b. If \(x\) is not in a subspace \(W\) , then \(x - {\rm{pro}}{{\rm{j}}_W}x\) is not zero.

c. In a \(QR\) factorization, say \(A = QR\) (when \(A\) has linearly

independent columns), the columns of \(Q\) form an

orthonormal basis for the column space of \(A\).

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

9. \(\left[ {\begin{aligned}{{}{}}3&{ - 5}&1\\1&1&1\\{ - 1}&5&{ - 2}\\3&{ - 7}&8\end{aligned}} \right]\)

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