Question: Show that orthogonal projection of a vector y onto a line L through the origin in \({\mathbb{R}^{\bf{2}}}\) does not depend on the choice of the nonzero u in L used in the formula for \({\bf{\hat y}}\). To do this, suppose y and u are given and \({\bf{\hat y}}\) has been computed by formula (2) in this section. Replace u in that formula by \(c{\bf{u}}\), where c is an unspecified nonzero scalar. Show that the new formula gives the same \({\bf{\hat y}}\).

Short Answer

Expert verified

The new formula gives the same \(\widehat {\bf{y}}\) because y does not depend on the choice of nonzero u in L.

Step by step solution

01

Write orthogonal projection of y onto line L

The orthogonal projection of yonto the line L can be expressed as:

\(\widehat {\bf{y}} = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}{\bf{u}}\)

02

Replace u with cu in the formula of \({\bf{\hat y}}\)

The formula \(\widehat {\bf{y}} = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}{\bf{u}}\) becomes:

\(\begin{array}{c}\widehat {\bf{y}} = \frac{{{\bf{y}} \cdot \left( {c{\bf{u}}} \right)}}{{\left( {c{\bf{u}}} \right) \cdot \left( {c{\bf{u}}} \right)}}\left( {c{\bf{u}}} \right)\\ = \frac{{c\left( {{\bf{y}} \cdot {\bf{u}}} \right)}}{{{c^2}\left( {{\bf{u}} \cdot {\bf{u}}} \right)}}\left( c \right){\bf{u}}\\ = \widehat {\bf{y}}\end{array}\)

Therefore, y does not depend on the choice of nonzero u in L.

Hence, it is proved that the new formula gives the same \(\widehat {\bf{y}}\).

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Most popular questions from this chapter

Find an orthonormal basis of the subspace spanned by the vectors in Exercise 4.

A simple curve that often makes a good model for the variable costs of a company, a function of the sales level \(x\), has the form \(y = {\beta _1}x + {\beta _2}{x^2} + {\beta _3}{x^3}\). There is no constant term because fixed costs are not included.

a. Give the design matrix and the parameter vector for the linear model that leads to a least-squares fit of the equation above, with data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\).

b. Find the least-squares curve of the form above to fit the data \(\left( {4,1.58} \right),\left( {6,2.08} \right),\left( {8,2.5} \right),\left( {10,2.8} \right),\left( {12,3.1} \right),\left( {14,3.4} \right),\left( {16,3.8} \right)\) and \(\left( {18,4.32} \right)\), with values in thousands. If possible, produce a graph that shows the data points and the graph of the cubic approximation.

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

  1. \(\left[ {\begin{array}{*{20}{c}}{ - 1}\\4\\{ - 3}\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}5\\2\\1\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\{ - 4}\\{ - 7}\end{array}} \right]\)

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, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

4. \(\left( {\begin{aligned}{{}{}}3\\{ - 4}\\5\end{aligned}} \right),\left( {\begin{aligned}{{}{}}{ - 3}\\{14}\\{ - 7}\end{aligned}} \right)\)

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