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

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.

Suppose radioactive substance A and B have decay constants of \(.02\) and \(.07\), respectively. If a mixture of these two substances at a time \(t = 0\) contains \({M_A}\) grams of \(A\) and \({M_B}\) grams of \(B\), then a model for the total amount of mixture present at time \(t\) is

\(y = {M_A}{e^{ - .02t}} + {M_B}{e^{ - .07t}}\) (6)

Suppose the initial amounts \({M_A}\) and are unknown, but a scientist is able to measure the total amounts present at several times and records the following points \(\left( {{t_i},{y_i}} \right):\left( {10,21.34} \right),\left( {11,20.68} \right),\left( {12,20.05} \right),\left( {14,18.87} \right)\) and \(\left( {15,18.30} \right)\).

a.Describe a linear model that can be used to estimate \({M_A}\) and \({M_B}\).

b. Find the least-squares curved based on (6).

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

Suppose \(A = QR\), where \(R\) is an invertible matrix. Showthat \(A\) and \(Q\) have the same column space.

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.

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

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