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22. Question: In Exercises 21 and 22, all vectors are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

  1. If W is a subspace of \({\mathbb{R}^n}\) and if v is in both W and \({W^ \bot }\), then v must be the zero vector.
  2. In the Orthogonal Decomposition Theorem, each term in formula (2) for \(\widehat {\mathop{\rm y}\nolimits} \) is itself an orthogonal projection of y onto a subspace of \(W\).
  3. If \({\mathop{\rm y}\nolimits} = {{\bf{z}}_1} + {{\bf{z}}_2}\), where \({{\bf{z}}_1}\) is in a subspace W and \({{\bf{z}}_2}\) is in \({W^ \bot }\), then \({{\bf{z}}_1}\) must be the orthogonal projection of \({\mathop{\rm y}\nolimits} \) onto W.
  4. The best approximation to y by elements of a subspace W is given by the vector \({\mathop{\rm y}\nolimits} - {{\mathop{\rm proj}\nolimits} _W}{\mathop{\rm y}\nolimits} \).
  5. If an \(n \times p\) matrix \(U\) has orthonormal columns, then \(U{U^T}{\mathop{\rm x}\nolimits} = {\mathop{\rm x}\nolimits} \) for all x in \({\mathbb{R}^n}\).

Short Answer

Expert verified
  1. The given statement is true.
  2. The given statement is true.
  3. The given statement is true.
  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)

The equality \(\widehat {\bf{y}} - {\widehat {\bf{y}}_1} = {{\bf{z}}_1} - {\bf{z}}\) demonstrates that the vector \({\mathop{\rm v}\nolimits} = \widehat {\bf{y}} - {\widehat {\bf{y}}_1}\) is in W and is in \({W^ \bot }\). Thus, \({\bf{v}} = 0\).

Thus, the given statement (a) is true.

02

Check whether the statement is true or false

b)

If \(\dim > 1\), then every term in \(\widehat {\bf{y}}\) is itself an orthogonal projection of y onto a subspace spanned by several of the u’s in the basis for \(W\).

Thus, the given statement (b) is true.

03

Check whether the statement is true or false

c)

By Theorem 8, there is unique orthogonal decomposition.

Thus, the given statement (c) is true.

04

Check whether the statement is true or false

d)

The best approximation theorem states that the \({{\mathop{\rm proj}\nolimits} _w}{\bf{y}}\) is the best approximation to y.

Thus, the given statement (d) is false.

05

Check whether the statement is true or false

e)

This statement holds only when the column space of \(U\) is in \({\bf{x}}\). When \(n > p\)then, the column space of \(U\) would not be all of \({\mathbb{R}^n}\). Therefore, the statement would not be true for all \({\bf{x}}\) in \({\mathbb{R}^n}\).

Thus, the given statement (e) is false.

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Most popular questions from this chapter

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

17. a.If \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},{{\bf{v}}_3}} \right\}\) is an orthogonal basis for\(W\), then multiplying

\({v_3}\)by a scalar \(c\) gives a new orthogonal basis \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},c{{\bf{v}}_3}} \right\}\).

b. The Gram–Schmidt process produces from a linearly independent

set \(\left\{ {{{\bf{x}}_1}, \ldots ,{{\bf{x}}_p}} \right\}\)an orthogonal set \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) with the property that for each \(k\), the vectors \({{\bf{v}}_1}, \ldots ,{{\bf{v}}_k}\) span the same subspace as that spanned by \({{\bf{x}}_1}, \ldots ,{{\bf{x}}_k}\).

c. If \(A = QR\), where \(Q\) has orthonormal columns, then \(R = {Q^T}A\).

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

5. \(\left( {\begin{aligned}{{}{}}1\\{ - 4}\\0\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{}}7\\{ - 7}\\{ - 4}\\1\end{aligned}} \right)\)

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 .

(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 \(y\)-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 -values.

19. Justify the equation \(SS\left( T \right) = SS\left( R \right) + SS\left( E \right)\). (Hint: Use a theorem, and explain why the hypotheses of the theorem are satisfied.) This equation is extremely important in statistics, both in regression theory and in the analysis of variance.

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

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

6. \(\left( {\begin{aligned}{{}}3\\{ - 1}\\2\\{ - 1}\end{aligned}} \right),\left( {\begin{aligned}{{}}{ - 5}\\9\\{ - 9}\\3\end{aligned}} \right)\)

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