Chapter 7: Q-7.3-11E (page 395)
Suppose \({\rm{x}}\)is a unit eigenvector of a matrix \(A\) corresponding to an eigenvalue 3. What is the value of \({{\rm{x}}^T}A{\rm{x}}\)?
Short Answer
The required value is: \({{\rm{x}}^T}A{\rm{x}} = 3\).
Chapter 7: Q-7.3-11E (page 395)
Suppose \({\rm{x}}\)is a unit eigenvector of a matrix \(A\) corresponding to an eigenvalue 3. What is the value of \({{\rm{x}}^T}A{\rm{x}}\)?
The required value is: \({{\rm{x}}^T}A{\rm{x}} = 3\).
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Get started for freeQuestion: 11. Given multivariate data \({X_1},................,{X_N}\) (in \({\mathbb{R}^p}\)) in mean deviation form, let \(P\) be a \(p \times p\) matrix, and define \({Y_k} = {P^T}{X_k}{\rm{ for }}k = 1,......,N\).
Find the matrix of the quadratic form. Assume x is in \({\mathbb{R}^2}\).
a. \(5x_1^2 + 16{x_1}{x_2} - 5x_2^2\)
b. \(2{x_1}{x_2}\)
Question: In Exercises 1 and 2, convert the matrix of observations to mean deviation form, and construct the sample covariance matrix.
\(1.\,\,\left( {\begin{array}{*{20}{c}}{19}&{22}&6&3&2&{20}\\{12}&6&9&{15}&{13}&5\end{array}} \right)\)
(M) Orhtogonally diagonalize the matrices in Exercises 37-40. To practice the methods of this section, do not use an eigenvector routine from your matrix program. Instead, use the program to find the eigenvalues, and for each eigenvalue \(\lambda \), find an orthogonal basis for \({\bf{Nul}}\left( {A - \lambda I} \right)\), as in Examples 2 and 3.
38. \(\left( {\begin{aligned}{{}}{.{\bf{63}}}&{ - .{\bf{18}}}&{ - .{\bf{06}}}&{ - .{\bf{04}}}\\{ - .{\bf{18}}}&{.{\bf{84}}}&{ - .{\bf{04}}}&{.{\bf{12}}}\\{ - .{\bf{06}}}&{ - .{\bf{04}}}&{.{\bf{72}}}&{ - .{\bf{12}}}\\{ - .{\bf{04}}}&{.{\bf{12}}}&{ - .{\bf{12}}}&{.{\bf{66}}}\end{aligned}} \right)\)
Let u be a unit vector in \({\mathbb{R}^n}\), and let \(B = {\bf{u}}{{\bf{u}}^T}\).
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