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

Short Answer

Expert verified

The least-square error is \(2\sqrt 5 \).

Step by step solution

01

Write the results of Exercise 3

From Exercise 3, the normal equation is:

\(\left( {\begin{aligned}{{}{}}6&6\\6&{42}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right) = \left( {\begin{aligned}{{}{}}6\\{ - 6}\end{aligned}} \right)\)

The \({\bf{\hat x}}\) component is\(\left( {\begin{aligned}{{}{}}{\frac{4}{3}}\\{ - \frac{1}{3}}\end{aligned}} \right)\).

02

Find the matrix \(A{\bf{\hat x}} - {\bf{b}}\)

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

03

Find the magnitude of  \(\left| {A{\bf{\hat x}} - {\bf{b}}} \right|\)

The value of \(\left| {A{\bf{\hat x}} - {\bf{b}}} \right|\) can be calculated as:

\(\begin{aligned}{}\left| {A{\bf{\hat x}} - {\bf{b}}} \right| &= \sqrt {{{\left( 1 \right)}^2} + {{\left( 3 \right)}^2} + {{\left( { - 3} \right)}^2} + {{\left( 1 \right)}^2}} \\ &= \sqrt {1 + 9 + 9 + 1} \\ &= \sqrt {20} \\ &= 2\sqrt 5 \end{aligned}\)

Thus, the least square error is \(2\sqrt 5 \).

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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 \(SS\left( R \right)\).

(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 -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 \(y\)-values.

20. Show that \({\left\| {X\hat \beta } \right\|^2} = {\hat \beta ^T}{X^T}{\bf{y}}\). (Hint: Rewrite the left side and use the fact that \(\hat \beta \) satisfies the normal equations.) This formula for is used in statistics. From this and from Exercise 19, obtain the standard formula for \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

Compute the quantities in Exercises 1-8 using the vectors

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

7. \(\left\| {\mathop{\rm w}\nolimits} \right\|\)

Let \({\mathbb{R}^{\bf{2}}}\) have the inner product of Example 1. Show that the Cauchy-Schwarz inequality holds for \({\bf{x}} = \left( {{\bf{3}}, - {\bf{2}}} \right)\) and \({\bf{y}} = \left( { - {\bf{2}},{\bf{1}}} \right)\). (Suggestion: Study \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^{\bf{2}}}\).)

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

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.

3. \(\left( {\begin{aligned}{{}{}}2\\{ - 5}\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{}}4\\{ - 1}\\2\end{aligned}} \right)\)

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