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In exercises 7-10, show that {u1, u2} or {u1,u2,u3} is an orthogonal basis for \({\mathbb{R}^2}\) or \({\mathbb{R}^3}\), respectively. Then express x as a linear combination of the u.

7. \[{u_1} = \left[ {\begin{align}2\\{ - 3}\end{align}} \right]\], \[{u_2} = \left[ {\begin{align}6\\4\end{align}} \right]\], and \[x = \left[ {\begin{align}9\\{ - 7}\end{align}} \right]\]

Short Answer

Expert verified

The required linear combination is, \[x = 3{u_1} + \frac{1}{2}{u_2}\].

Step by step solution

01

Linear combination definition

Let the set of vectors \({u_1},.....,{u_p}\) be an orthogonal basis for a subspace \(W\) of \({\mathbb{R}^n}\) and the linear combination is given by \(y = {c_1}{u_1} + ..... + {c_p}{u_p}\) , then the weights in the linear combination are given as \({c_j} = \frac{{y \cdot {u_j}}}{{{u_j} \cdot {u_j}}}\), for each \(y\) in \(W\).

02

Check for orthogonality of given vectors

Find \({u_1} \cdot {u_2}\) as follows:

\(\begin{array}{c}{u_1} \cdot {u_2} = \left( 2 \right)\left( 6 \right) + \left( { - 3} \right)\left( 4 \right)\\ = 12 - 12\\ = 0\end{array}\)

Hence, the vectors are orthogonal to each other, as the vectors are non-zero and linearly independent. Therefore, the given set form a basis for \({\mathbb{R}^2}\).

03

Express x as a linear combination

The vector x can be expressed as a linear combination as follows:

\[\begin{align}{c}x = \left( {\frac{{x \cdot {u_1}}}{{{u_1} \cdot {u_1}}}} \right){u_1} + \left( {\frac{{x \cdot {u_2}}}{{{u_2} \cdot {u_2}}}} \right){u_2}\\ = \left( {\frac{{\left( 9 \right)\left( 2 \right) + \left( { - 7} \right)\left( { - 3} \right)}}{{\left( 2 \right)\left( 2 \right) + \left( { - 3} \right)\left( { - 3} \right)}}} \right){u_1} + \left( {\frac{{\left( 9 \right)\left( 6 \right) + \left( { - 7} \right)\left( 4 \right)}}{{\left( 6 \right)\left( 6 \right) + \left( 4 \right)\left( 4 \right)}}} \right){u_2}\\ = \left( {\frac{{18 + 21}}{{4 + 9}}} \right){u_1} + \left( {\frac{{54 - 28}}{{36 + 16}}} \right){u_2}\\ = \left( {\frac{{18 + 2}}{{4 + 9}}} \right){u_1} + \left( {\frac{{54 - 28}}{{36 + 16}}} \right){u_2}\,\\ = 3{u_1} + \frac{1}{2}{u_2}\end{align}\]

Hence, \[x = 3{u_1} + \frac{1}{2}{u_2}\].

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Most popular questions from this chapter

Compute the least-squares error associated with the least square solution found in Exercise 3.

Compute the quantities in Exercises 1-8 using the vectors

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

  1. \({\mathop{\rm u}\nolimits} \cdot {\mathop{\rm u}\nolimits} ,{\rm{ }}{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm u}\nolimits} ,\,\,{\mathop{\rm and}\nolimits} \,\,\frac{{{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm u}\nolimits} }}{{{\mathop{\rm u}\nolimits} \cdot {\mathop{\rm u}\nolimits} }}\)

In Exercises 1-4, find a least-sqaures solution of \(A{\bf{x}} = {\bf{b}}\) by (a) constructing a normal equations for \({\bf{\hat x}}\) and (b) solving for \({\bf{\hat x}}\).

2. \(A = \left( {\begin{aligned}{{}{}}{\bf{2}}&{\bf{1}}\\{ - {\bf{2}}}&{\bf{0}}\\{\bf{2}} {\bf{3}}\end{aligned}} \right)\), \(b = \left( {\begin{aligned}{{}{}}{ - {\bf{5}}}\\{\bf{8}}\\{\bf{1}}\end{aligned}} \right)\)

In Exercises 7โ€“10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]โ€™s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

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

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) \({\left\| {X\hat \beta } \right\|^2}\)โ€”the sum of the squares of the โ€œregression term.โ€ Denote this number by .

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

(iii) \({\left\| {\bf{y}} \right\|^2}\)โ€”the โ€œtotalโ€ 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.

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