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Show that the least-squares line \({\bf{y = }}{{\bf{\hat \beta }}_{\bf{0}}}{\bf{ + }}{{\bf{\hat \beta }}_{\bf{1}}}{\bf{x}}\)passes through the point\({\bf{(\bar x,\bar y)}}\).

Short Answer

Expert verified

The point \((\bar x,\bar y)\)passes through the least square line.

Step by step solution

01

Define of the least square line

The equation for the least-squares line is \(y = {\beta _0} + {\beta _1}x\).

The formula for the intercept and slope coefficients \({\beta _0}\) and \({\beta _1}\) are given as:

\(\begin{array}{l}{{\hat \beta }_1} = \frac{{\sum\limits_{i = 1}^n {\left( {{x_i} - \bar x} \right)} \left( {{y_i} - \bar y} \right)}}{{\sum\limits_{i = 1}^n {{{\left( {{x_i} - \bar x} \right)}^2}} }}\\{{\hat \beta }_0} = \bar y - {{\hat \beta }_1}\bar x\end{array}\)

Thus, the least-squares line can be re-written as,

\({\bf{y = }}\left( {{\bf{\bar y - }}{{{\bf{\hat \beta }}}_{\bf{1}}}{\bf{\bar x}}} \right){\bf{ + }}{{\bf{\hat \beta }}_{\bf{1}}}{\bf{x}}\)

02

Check if the point passes through the line

To show that the point \((\bar x,\bar y)\)satisfies the above equation.

In other words, least-squares line passes through the point. \((\bar x,\bar y)\).

Substitute the point \(x = \bar x\)into the above equation.

\(\begin{array}{c}y = \left( {\bar y - {{\hat \beta }_1}\bar x} \right) + {{\hat \beta }_1}\bar x\\ = \bar y - {{\hat \beta }_1}\bar x + {{\hat \beta }_1}\bar x\\ = \bar y\end{array}\)

Thus, the point \((\bar x,\bar y)\)actually satisfies the above equation.

Therefore, the point \((\bar x,\bar y)\)lies on the least square line.

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Most popular questions from this chapter

Question:For the conditions of Exercise \({\bf{12}}\), and the data in Table \({\bf{11}}{\bf{.2}}\), carry out a test of the following hypotheses.

\(\begin{array}{*{20}{c}}{{{\bf{H}}_{\bf{0}}}{\bf{:}}{{\bf{\beta }}_{\bf{2}}}{\bf{ = - 1}}}\\{{{\bf{H}}_{\bf{1}}}{\bf{:}}{{\bf{\beta }}_{\bf{2}}} \ne {\bf{ - 1}}}\end{array}\)

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

Prove that \({{\bf{\sigma }}^{{\bf{'2}}}}\), defined in Eq.\({\bf{(11}}{\bf{.5}}{\bf{.8)}}\)is an unbiased estimator of \({{\bf{\sigma }}^{\bf{2}}}\). You may assume that \({{\bf{S}}^{\bf{2}}}\)has a \({{\bf{X}}^{\bf{2}}}\) distribution with \({\bf{n - p}}\) degrees of freedom.

Question:For the conditions of Exercise \({\bf{3}}\), show that \({\bf{E(\hat \beta ) = \beta }}\)and \({\bf{Var(\hat \beta ) = }}{{\bf{\sigma }}^{\bf{2}}}{\bf{/}}\left( {\mathop {\sum {{\bf{x}}_{\bf{i}}^{\bf{2}}} }\limits_{{\bf{i = 1}}}^{\bf{n}} } \right)\).

Consider again the conditions of Exercise 12, and let\(\theta = {c_1}{\beta _1} - {\beta _0}\), where \({c_1}\) is a constant. Determine an unbiased estimator \(\hat \theta \) of \(\theta \). For what value of \({c_1}\) will the M.S.E. of \(\hat \theta \) be smallest?

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