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Use the transpose definition of the inner product to verify parts (b) and (c) of Theorem 1. Mention the appropriate facts from Chapter 2.

Short Answer

Expert verified

Using the definition of inner product, verified that:

  1. \(\left( {{\bf{u}} + {\bf{v}}} \right) \cdot {\bf{w}} = {\bf{u}} \cdot {\bf{w}} + {\bf{v}} \cdot {\bf{w}}\)
  2. \(\left( {c{\bf{u}}} \right) \cdot {\bf{v}} = c\left( {{\bf{u}} \cdot {\bf{v}}} \right)\)

Step by step solution

01

 Verification of theorem 1(b).

Part (b) of theorem 1 is,\(\left( {{\bf{u}} + {\bf{v}}} \right) \cdot {\bf{w}} = {\bf{u}} \cdot {\bf{w}} + {\bf{v}} \cdot {\bf{w}}\).

Prove it by using the definition of Inner Product.

\(\begin{aligned}{c}\left( {{\bf{u}} + {\bf{v}}} \right) \cdot {\bf{w}} = {\left( {{\bf{u}} + {\bf{v}}} \right)^T} \cdot {\bf{w}}\\ = \left( {{{\bf{u}}^T} + {{\bf{v}}^T}} \right) \cdot {\bf{w}}\\ = {{\bf{u}}^T} \cdot {\bf{w}} + {{\bf{v}}^T} \cdot {\bf{w}}\\ = {\bf{u}} \cdot {\bf{w}} + {\bf{v}} \cdot {\bf{w}}\end{aligned}\)

Hence, part (b) of theorem 1 has been verified.

02

 Verification of theorem 1(c).

Part (c) of theorem 1 is,\(\left( {c{\bf{u}}} \right) \cdot {\bf{v}} = c\left( {{\bf{u}} \cdot {\bf{v}}} \right)\).

Prove it by using the definition of Inner Product.

\(\begin{aligned}{c}\left( {c{\bf{u}}} \right) \cdot {\bf{v}} = {\left( {c{\bf{u}}} \right)^T} \cdot {\bf{v}}\\ = c\left( {{{\bf{u}}^T}{\bf{v}}} \right)\\ = c\left( {{\bf{u}} \cdot {\bf{v}}} \right)\end{aligned}\)

Hence, part (c) of theorem 1 has been verified.

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Most popular questions from this chapter

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

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 1-6, determine which sets of vectors are orthogonal.

\(\left[ {\begin{array}{*{20}{c}}5\\{ - 4}\\0\\3\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}{ - 4}\\1\\{ - 3}\\8\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\3\\5\\{ - 1}\end{array}} \right]\)

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

3. \(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{ - {\bf{2}}}\\{ - {\bf{1}}}&{\bf{2}}\\{\bf{0}}&{\bf{3}}\\{\bf{2}}&{\bf{5}}\end{aligned}} \right)\), \({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{3}}\\{\bf{1}}\\{ - {\bf{4}}}\\{\bf{2}}\end{aligned}} \right)\)

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