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Prove that\(\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\left( {{{\bf{c}}_{\bf{1}}}{{\bf{x}}_{\bf{i}}}{\bf{ + }}{{\bf{c}}_{\bf{2}}}} \right)}^{\bf{2}}}} {\bf{ = c}}_{\bf{1}}^{\bf{2}}\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\left( {{{\bf{x}}_{\bf{i}}}{\bf{ - \bar x}}} \right)}^{\bf{2}}}} {\bf{ + n}}{\left( {{{\bf{c}}_{\bf{1}}}{\bf{\bar x + }}{{\bf{c}}_{\bf{2}}}} \right)^{\bf{2}}}\).

Short Answer

Expert verified

It is proved that, \(\sum\limits_{i = 1}^n {{{\left( {{c_1}{x_i} + {c_2}} \right)}^2}} = c_1^2\sum\limits_{i = 1}^n {{{\left( {{x_i} - \bar x} \right)}^2}} + n{\left( {{c_1}\bar x + {c_2}} \right)^2}\)

Step by step solution

01

State the general expression

To prove:

\(\sum\limits_{i = 1}^n {{{\left( {{c_1}{x_i} + {c_2}} \right)}^2}} = c_1^2\sum\limits_{i = 1}^n {{{\left( {{x_i} - \bar x} \right)}^2}} + n{\left( {{c_1}\bar x + {c_2}} \right)^2}\)

The formula used to expand the expression,

\({\bf{\bar x = }}\frac{{\bf{1}}}{{\bf{n}}}\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\bf{x}}_{\bf{i}}}} \)then\(\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\bf{x}}_{\bf{i}}}} {\bf{ = n\bar x}}\).

02

Expand the expression \(\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\left( {{{\bf{c}}_{\bf{1}}}{{\bf{x}}_{\bf{i}}}{\bf{ + }}{{\bf{c}}_{\bf{2}}}} \right)}^{\bf{2}}}} \)

Expanding the left side first, we have that, \(\begin{array}{c}\sum\limits_{i = 1}^n {{{\left( {{c_1}{x_i} + {c_2}} \right)}^2}} = \sum\limits_{i = 1}^n {\left( {c_1^2x_i^2 + 2{c_1}{c_2}{x_i} + c_2^2} \right)} \\ = c_1^2\sum\limits_{i = 1}^n {x_i^2} + 2{c_1}{c_2}\sum\limits_{i = 1}^n {{x_i}} + nc_2^2\;\;\;\;\;\;\;\;\;\;\;\left( {\sum\limits_{i = 1}^n {{x_i}} = n\bar x} \right)\\ = c_1^2\sum\limits_{i = 1}^n {x_i^2} + 2n{c_1}{c_2}\bar x + nc_2^2\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;...\left( 1 \right)\;\;\;\;\end{array}\)

03

Expand the expression \({\bf{c}}_{\bf{1}}^{\bf{2}}\sum\limits_{{\bf{i = 1}}}^{\bf{n}} {{{\left( {{{\bf{x}}_{\bf{i}}}{\bf{ - \bar x}}} \right)}^{\bf{2}}}} {\bf{ + n}}{\left( {{{\bf{c}}_{\bf{1}}}{\bf{\bar x + }}{{\bf{c}}_{\bf{2}}}} \right)^{\bf{2}}}\)

`Now, expanding the right side, we have that,

\(\begin{array}{c}c_1^2\sum\limits_{i = 1}^n {{{\left( {{x_i} - \bar x} \right)}^2}} + n{\left( {{c_1}\bar x + {c_2}} \right)^2} = c_1^2\sum\limits_{i = 1}^n {\left( {x_i^2 - 2{x_i}\bar x + {{\bar x}^2}} \right)} + n\left( {c_1^2{{\bar x}^2} + 2{c_1}{c_2}\bar x + c_2^2} \right)\\ = c_1^2 \cdot \sum\limits_{i = 1}^n {x_i^2} - 2c_1^2\bar x\underbrace {\sum\limits_{i = 1}^n {{x_i}} }_{ = n \cdot \bar x} + nc_1^2{{\bar x}^2} + nc_1^2{{\bar x}^2} + 2n{c_1}{c_2}\bar x + nc_2^2\\ = c_1^2\sum\limits_{i = 1}^n {x_i^2} - 2nc_1^2{{\bar x}^2} + 2nc_1^2{{\bar x}^2} + 2n{c_1}{c_2}\bar x + nc_2^2\\ = c_1^2\sum\limits_{i = 1}^n {x_i^2} + 2n{c_1}{c_2}\bar x + nc_2^2\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;...\left( 2 \right)\end{array}\) \(\) \(\)

\(\)

Clearly, expressions labeled with (1) and (2) are equal, which means that the two initial expressions are equal as well, which proves the given equality.

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Most popular questions from this chapter

Suppose that each of two different varieties of corn is treated with two different types of fertilizer in order to compare the yields, and that \(K\)independent replications are obtained for each of the four combinations. Let \({X_{ijk}}\)denote the yield on the \(K\)The replication of the combination of variety \(i\) with fertilizer\(j(i = 1,2;j = 1,2\);\(k = 1, \ldots ,K\)). Assume that all the observations are independent and normally distributed, each distribution has the same unknown variance, and \({\bf{E}}\left( {{{\bf{X}}_{{\bf{ijk}}}}} \right){\bf{ = }}{{\bf{\mu }}_{{\bf{ij}}}}\)for \(k = 1, \ldots ,K.\) Explain in words what the following hypotheses mean, and describe how to carry out a test of them:

\({{\bf{H}}_{\bf{0}}}{\bf{:}}\;\;\;{{\bf{\mu }}_{{\bf{11}}}}{\bf{ - }}{{\bf{\mu }}_{{\bf{12}}}}{\bf{ = }}{{\bf{\mu }}_{{\bf{21}}}}{\bf{ - }}{{\bf{\mu }}_{{\bf{22}}}}{\bf{, }}\)

\({H_1}\): The hypothesis \({H_0}\) is not true.

Question: Suppose that in a problem of simple linear regression, a confidence interval with confidence coefficient \({\bf{1 - }}{{\bf{\alpha }}_{\bf{0}}}\)\(\left( {{\bf{0 < }}{{\bf{\alpha }}_{\bf{0}}}{\bf{ < 1}}} \right)\) is constructed for the height of the regression line at a given value of \(x\). Show that the length of this confidence interval is shortest when \({\bf{x = \bar x}}\).

Consider again the conditions of Exercise 12, and suppose that it is desired to estimate the value of \(\theta = 5 - 4{\beta _0} + {\beta _1}\). Find an unbiased estimator \(\hat \theta \) of

\(\theta \). Determine the value of \(\hat \theta \)and the M.S.E. of\(\hat \theta \).

Suppose it is desired to estimate the proportion of persons in a large population with a certain characteristic. A random sample of 100 persons is selected from the population without replacement, and the proportion \(\overline X \)of persons in the sample who have the characteristic is observed. Show that, no matter how large the population, the standard deviation \(\overline X \)is at most 0.05.

Question:For the conditions of Exercise \({\bf{12}}\), and the data in Table \({\bf{11}}{\bf{.2}}\), determine the value of \({{\bf{R}}^{\bf{2}}}\), as defined by Eq.\({\bf{(11}}{\bf{.5}}{\bf{.26)}}\).

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