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23. Question: In Exercises 23 and 24, all vectors are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

  1. Not every linearly independent set in \({\mathbb{R}^n}\) is an orthogonal set.
  2. If y is a linear combination of nonzero vectors from an orthogonal set, then the weights in the linear combination can be computed without row operations on a matrix.
  3. If the vectors in an orthogonal set of nonzero vectors are normalized, then some of the new vectors may not be orthogonal.
  4. A matrix with orthonormal columns is an orthogonal matrix.
  5. If L is a line through 0 and if \(\widehat {\mathop{\rm y}\nolimits} \) is the orthogonal projection of y onto L, then \(\left\| {\widehat {\mathop{\rm y}\nolimits} } \right\|\) gives the distance from y to L.

Short Answer

Expert verified
  1. The given statement is true.
  2. The given statement is true.
  3. The given statement is false.
  4. The given statement is false.
  5. The given statement is false.

Step by step solution

01

Check whether the statement is true or false

a)

Consider a counterexample that \({\bf{y}} = \left( {\begin{array}{*{20}{c}}7\\6\end{array}} \right)\)and \({\bf{u}} = \left( {\begin{array}{*{20}{c}}4\\2\end{array}} \right)\). Then, \(\left\{ {{\bf{y}},{\bf{u}}} \right\}\) is linearly independent sets but not an orthogonal set because \({\bf{y}} \cdot {\bf{u}} \ne 0\).

Thus, the given statement (a) is true.

02

Check whether the statement is true or false

b)

Theorem 5states that consider \(\left\{ {{{\mathop{\rm u}\nolimits} _1},{{\mathop{\rm u}\nolimits} _2}, \ldots ,{{\mathop{\rm u}\nolimits} _p}} \right\}\) as anorthogonal basisfor a subspace \(W\) of \({\mathbb{R}^n}\), then theweightsin the linear combination \({\mathop{\rm y}\nolimits} = {c_1}{{\bf{u}}_1} + \cdots + {c_1}{{\bf{u}}_p}\)for every y in \(W\) is denoted by \({c_j} = \frac{{{\bf{y}} \cdot {{\bf{u}}_j}}}{{{{\bf{u}}_j} \cdot {{\bf{u}}_j}}}\left( {j = 1, \ldots ,p} \right)\).

Thus, the given statement (b) is true.

03

Check whether the statement is true or false

c)

If the nonzero vector in an orthogonal set isnormalized to have unit length, then the new vectors remain orthogonal.

Thus, the given statement (c) is false.

04

Check whether the statement is true or false

d)

Asquare invertible matrix\(U\)is an orthogonal matrix, such that \({U^{ - 1}} = {U^T}\). Such a square matrix contains orthonormal columns.

The square matrix with orthonormal columns is orthogonal.

Thus, the given statement (d) is false.

05

Check whether the statement is true or false

e)

It is known that the distance fromy to L is equal to the length of the perpendicular line segment from yto the orthogonal projection \(\widehat {\bf{y}}\), that is \(\left\| {{\bf{y}} - \widehat {\bf{y}}} \right\|\).

Thus, the given statement (e) is false.

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Most popular questions from this chapter

Find the distance between \({\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{10}\\{ - 3}\end{aligned}} \right)\) and \({\mathop{\rm y}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\{ - 5}\end{aligned}} \right)\).

Question: In exercises 1-6, determine which sets of vectors are orthogonal.

\(\left[ {\begin{align}1\\{ - 2}\\1\end{align}} \right]\), \(\left[ {\begin{align}0\\1\\2\end{align}} \right]\), \(\left[ {\begin{align}{ - 5}\\{ - 2}\\1\end{align}} \right]\)

In Exercises 11 and 12, find the closest point to\[{\bf{y}}\]in the subspace\[W\]spanned by\[{{\bf{v}}_1}\], and\[{{\bf{v}}_2}\].

11.\[y = \left[ {\begin{aligned}3\\1\\5\\1\end{aligned}} \right]\],\[{{\bf{v}}_1} = \left[ {\begin{aligned}3\\1\\{ - 1}\\1\end{aligned}} \right]\],\[{{\bf{v}}_2} = \left[ {\begin{aligned}1\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

Suppose \(A = QR\) is a \(QR\) factorization of an \(m \times n\) matrix

A (with linearly independent columns). Partition \(A\) as \(\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right]\), where \({A_1}\) has \(p\) columns. Show how to obtain a \(QR\) factorization of \({A_1}\), and explain why your factorization has the appropriate properties.

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) \({\left\| {X\hat \beta } \right\|^2}\)鈥攖he sum of the squares of the 鈥渞egression term.鈥 Denote this number by \(SS\left( R \right)\).

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

(iii) \({\left\| {\bf{y}} \right\|^2}\)鈥攖he 鈥渢otal鈥 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 \(SS\left( E \right)\):

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

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