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The method of completing the square is used several times in this text. It is a useful method for combining several quadratic and linear polynomials into a perfect square plus a constant. Prove the following identity, which is one general form of completing the square:

\(\begin{array}{c}\sum\limits_{i = 1}^n {{a_i}{{\left( {x - {b_i}} \right)}^2} + cx} \\\,\,\,\, = \left( {\sum\limits_{i = 1}^n {{a_i}} } \right){\left( {x - \frac{{\sum\limits_{i = 1}^n {{a_i}{b_i} - \frac{c}{2}} }}{{\sum\limits_{i = 1}^n {{a_i}} }}} \right)^2}\\\,\,\,\,\,\, + {\sum\limits_{i = 1}^n {{a_i}\left( {{b_i} - \frac{{\sum\limits_{i = 1}^n {{a_i}{b_i}} }}{{\sum\limits_{i = 1}^n {{a_i}} }}} \right)} ^2}\\\,\,\,\,\, + {\left( {\sum\limits_{i = 1}^n {{a_i}} } \right)^{ - 1}}\left[ {c\sum\limits_{i = 1}^n {{a_i}{b_i} - \frac{{{c^2}}}{4}} } \right]\\if\,\,\sum\limits_{i = 1}^n {{a_i} \ne 0.} \end{array}\)

Short Answer

Expert verified

Proved.

Step by step solution

01

Given information

Completing the square is a useful method for combining several quadratic and linear polynomials into a perfect square plus constant.

02

Proving part

First, expand the left-hand side of the equation to get,

\(\begin{aligned}\sum\limits_{i = 1}^n {{a_i}{{\left( {x - {b_i}} \right)}^2} + cx} &= cx + \sum\limits_{i = 1}^n {{a_i}\left[ {{x^2} - 2{b_i}x + {b_i}^2} \right]} \\ &= cx + \sum\limits_{i = 1}^n {\left[ {{a_i}{x^2} - 2{a_i}{b_i}x + {a_i}{b_i}^2} \right]} ...\left( 1 \right)\end{aligned}\)

Now collect all the square terms in x.

The coefficient of \({x^2}\) is \(\sum\limits_{i = 1}^n {{a_i}} \)

The coefficient of x is \(c - 2\sum\limits_{i = 1}^n {{a_i}{b_i}} \)

The constant term is \(\sum\limits_{i = 1}^n {{a_i}b_i^2} \)

So,

\({x^2}\sum\limits_{i = 1}^n {{a_i} + x\left[ {c - 2\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right] + \sum\limits_{i = 1}^n {{a_i}b_i^2} } ...\left( 2 \right)\)

Next, expand each term on the right side of the original equation to produce

Then,

\(\begin{aligned}\sum\limits_{i = 1}^n {{a_i}{{\left( {x - {b_i}} \right)}^2} + cx} &= \left( {\sum\limits_{i = 1}^n {{a_i}} } \right)\left( {{x^2} - 2x{b_i} + b_i^2} \right) + cx\\ &= {x^2}\sum\limits_{i = 1}^n {{a_i} + x\left[ {c - 2\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right] + \sum\limits_{i = 1}^n {{a_i}b_i^2} } \\ &= \left( {\sum\limits_{i = 1}^n {{a_i}} } \right)\left\{ {{x^2} - 2x\left( {\frac{{\sum\limits_{i = 1}^n {{a_i}{b_i} - \frac{c}{2}} }}{{\sum\limits_{i = 1}^n {{a_i}} }}} \right)} \right\}\\ &+ \sum\limits_{i = 1}^n {{a_i}b_i^2} + \frac{{{{\left( {\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right)}^2}}}{{\sum\limits_{i = 1}^n {{a_i}} }} - \frac{{{{\left( {\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right)}^2}}}{{\sum\limits_{i = 1}^n {{a_i}} }}\\ &- \frac{{c\sum\limits_{i = 1}^n {{a_i}{b_i}} }}{{\sum\limits_{i = 1}^n {{a_i}} }} + \frac{{\frac{{{c^2}}}{4}}}{{\sum\limits_{i = 1}^n {{a_i}} }} + \frac{{c\sum\limits_{i = 1}^n {{a_i}{b_i}} }}{{\sum\limits_{i = 1}^n {{a_i}} }} - \frac{{\frac{{{c^2}}}{4}}}{{\sum\limits_{i = 1}^n {{a_i}} }}\\ &= \left( {\sum\limits_{i = 1}^n {{a_i}} } \right)\left( {{x^2} - 2x\frac{{\sum\limits_{i = 1}^n {{a_i}{b_i} - \frac{c}{2}} }}{{\sum\limits_{i = 1}^n {{a_i}} }}} \right) + \frac{{{{\left( {\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right)}^2} - c\sum\limits_{i = 1}^n {{a_i}{b_i}} + \frac{{{c^2}}}{4}}}{{\sum\limits_{i = 1}^n {{a_i}} }}\\\,\,\,\,\,\, &+ \sum\limits_{i = 1}^n {{a_i}b_i^2 - \frac{{{{\left( {\sum\limits_{i = 1}^n {{a_i}{b_i}} } \right)}^2}}}{{\sum\limits_{i = 1}^n {{a_i}} }} + \frac{{c\sum\limits_{i = 1}^n {{a_i}{b_i} - \frac{{{c^2}}}{4}} }}{{\sum\limits_{i = 1}^n {{a_i}} }}} \end{aligned}\)

Hence, [Proved].

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Most popular questions from this chapter

It is said that a random variable has the Weibull distribution with parameters a and b (a > 0 and b > 0) if X has a continuous distribution for which the p.d.f. f (x|a, b) is as follows:

\({\bf{f}}\left( {{\bf{x|a,b}}} \right){\bf{ = }}\frac{{\bf{b}}}{{{{\bf{a}}^{\bf{b}}}}}{{\bf{x}}^{{\bf{b - 1}}}}{{\bf{e}}^{{\bf{ - }}{{\left( {\frac{{\bf{x}}}{{\bf{a}}}} \right)}^{\bf{b}}}}}\,{\bf{,x > 0}}\)

Show that if X has this Weibull distribution, then the random variable \({{\bf{X}}^{\bf{b}}}\) has the exponential distribution with parameter \({\bf{\beta = }}{{\bf{a}}^{{\bf{ - b}}}}\)

Suppose that two random variables \({{\bf{X}}_{\bf{1}}}{\rm{ }}{\bf{and}}{\rm{ }}{{\bf{X}}_{\bf{2}}}\)have a bivariate normal distribution, and two other random variables \({{\bf{Y}}_{\bf{1}}}{\rm{ }}{\bf{and}}{\rm{ }}{{\bf{Y}}_{\bf{2}}}\)are defined as follows:

\(\begin{array}{*{20}{l}}{{{\bf{Y}}_{\bf{1}}}{\rm{ }} = {\rm{ }}{{\bf{a}}_{{\bf{11}}}}{{\bf{X}}_{\bf{1}}}{\rm{ }} + {\rm{ }}{{\bf{a}}_{{\bf{12}}}}{{\bf{X}}_{\bf{2}}}{\rm{ }} + {\rm{ }}{{\bf{b}}_{\bf{1}}},}\\{{{\bf{Y}}_{\bf{2}}}{\rm{ }} = {\rm{ }}{{\bf{a}}_{{\bf{21}}}}{{\bf{X}}_{\bf{1}}}{\rm{ }} + {\rm{ }}{{\bf{a}}_{{\bf{22}}}}{{\bf{X}}_{\bf{2}}}{\rm{ }} + {\rm{ }}{{\bf{b}}_{\bf{2}}},}\end{array}\)

where

\(\left| {\begin{array}{*{20}{l}}{{{\bf{a}}_{{\bf{11}}}}{\rm{ }}{{\bf{a}}_{{\bf{12}}}}}\\{{{\bf{a}}_{{\bf{21}}}}{\rm{ }}{{\bf{a}}_{{\bf{22}}}}}\end{array}} \right|\)

Show that \({{\bf{Y}}_{\bf{1}}}{\rm{ }}{\bf{and}}{\rm{ }}{{\bf{Y}}_{\bf{2}}}\) also have a bivariate normal distribution.

If the temperature in degrees Fahrenheit at a certain location is normally distributed with a mean of 68 degrees and a standard deviation of 4 degrees, what is the distribution of the temperature in degrees Celsius at the same location?

1. Consider a daily lottery as described in Example 5.5.4.

a. Compute the probability that two particular days ina row will both have triples.

b. Suppose that we observe triples on a particular day. Compute the conditional probability that we observe triples again the next day.

Find the 0.5, 0.25, 0.75, 0.1, and 0.9 quantiles of the standard normal distribution.

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