Given: \(\displaystyle{n}=\ \text{Sample size}\ ={44}\) a) Lot Size is on the horizontal axis and value is on the vertical axis. b) It is reasonable to find a regression lien for the data if there is no strong curvature present in the scatterplot. We note that there is no strong curvature in the scatterplot of part (a) and thus it is reasonable to find a regression line for the data. c) Let us first determine the necessary sums: \(\displaystyle\sum\ {x}_{{{i}}}={101.93}\)

\(\displaystyle\sum\ {{x}_{{{i}}}^{{{2}}}}={241.5221}\)

\(\displaystyle\sum\ {y}_{{{i}}}={19689}\)

\(\displaystyle\sum\ {x}_{{{i}}}{y}_{{{i}}}={45973.23}\) Next, we can determine \(\displaystyle{S}_{{xx}}\) and \(\displaystyle{S}_{{{x}{y}}}\)

\(\displaystyle{S}_{{xx}}=\ \sum\ {{x}_{{{i}}}^{{{2}}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}^{{{2}}}}}{{{n}}}}={241.5221}\ -\ {\frac{{{101.93}^{{{2}}}}}{{{44}}}}={5.3920}\)

\(\displaystyle{S}_{{{x}{y}}}=\ \sum\ {x}_{{{i}}}{y}_{{{i}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}{\left(\sum\ {y}_{{{i}}}\right)}}}{{{n}}}}={45973.23}\ -\ {\frac{{{101.93}\ \cdot\ {19689}}}{{{44}}}}={361.8716}\) The estimate b of the slope \(\displaystyle\beta\) is the ratio of \(\displaystyle{S}_{{{x}{y}}}\) and \(\displaystyle{S}_{{xx}}\): \(\displaystyle{b}=\ {\frac{{{S}_{{{x}{y}}}}}{{{S}_{{xx}}}}}=\ {\frac{{{361.8716}}}{{{5.3920}}}}={67.1128}\) The mean is the sum of all values divided by the number of values: \(\displaystyle\overline{{{x}}}=\ {\frac{{\sum\ {x}_{{{i}}}}}{{{n}}}}=\ {\frac{{{101.93}}}{{{44}}}}={2.3166}\)

\(\displaystyle\overline{{{y}}}=\ {\frac{{\sum\ {y}_{{{i}}}}}{{{n}}}}=\ {\frac{{{19689}}}{{{44}}}}={447.4773}\) The estimate a of the intercept \(\displaystyle\alpha\) is the average of y decreased by the product of the estimate of the slope and the average of x. \(\displaystyle{a}=\ \overline{{{y}}}\ -\ {b}\ \overline{{{x}}}={447.4773}\ -\ {67.1128}\ \cdot\ {2.3166}={292.0043}\) General least-squares equation: \(\displaystyle\hat{{{y}}}=\ \alpha\ +\ \beta\ {x}\). Replace \(\displaystyle\alpha\) by \(\displaystyle{a}={292.0043}\) and \(\displaystyle\beta\) by \(\displaystyle{b}={67.1128}\) in the general least-squares equation: \(\displaystyle{y}={a}\ +\ {b}{x}={292.0043}\ +\ {67.1128}{x}\) d) There appear to be no outliers, because the right most point lies much futher to the right than the other points in the graph. The outlier also appears to be a potential outlier, because it is possible that this point pulls the regression line down. e) Let us first determine the necessary sums: \(\displaystyle\sum\ {x}_{{{i}}}={98.31}\)

\(\displaystyle\sum\ {{x}_{{{i}}}^{{{2}}}}={228.4177}\)

\(\displaystyle\sum\ {y}_{{{i}}}={19314}\)

\(\displaystyle\sum\ {x}_{{{i}}}{y}_{{{i}}}={44615.73}\) Next, we can determine \(\displaystyle{S}_{{\times}}\) and \(\displaystyle{S}_{{{x}{y}}}\)

\(\displaystyle{S}_{{\times}}=\ \sum\ {{x}_{{{i}}}^{{{2}}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}^{{{2}}}}}{{{n}}}}={228.4177}\ -\ {\frac{{{98.31}^{{{2}}}}}{{{43}}}}={3.6536}\)

\(\displaystyle{S}_{{{x}{y}}}=\ \sum\ {x}_{{{i}}}{y}_{{{i}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}{\left(\sum\ {y}_{{{i}}}\right)}}}{{{n}}}}={44615.73}\ -\ {\frac{{{98.31}\ \cdot\ {19314}}}{{{43}}}}={458.5360}\) The estimate b of the slope \(\displaystyle\beta\) is the ratio of \(\displaystyle{S}_{{{x}{y}}}\) and \(\displaystyle{S}_{{\times}}\): \(\displaystyle{b}=\ {\frac{{{S}_{{{x}{y}}}}}{{{S}_{{\times}}}}}=\ {\frac{{{458.5360}}}{{{3.6536}}}}={125.5024}\) The mean is the sum of all values divided by the number of values: \(\displaystyle\overline{{{x}}}=\ {\frac{{\sum\ {x}_{{{i}}}}}{{{n}}}}=\ {\frac{{{98.31}}}{{{43}}}}={2.2863}\)

\(\displaystyle\overline{{{y}}}=\ {\frac{{\sum\ {y}_{{{i}}}}}{{{n}}}}=\ {\frac{{{19314}}}{{{43}}}}={449.1628}\) The estimate a of the intercept \(\displaystyle\alpha\) is the average of y decreased by the product of the estimate of the slope and the average of x. \(\displaystyle{a}=\ \overline{{{y}}}\ -\ {b}\ \overline{{{x}}}={449.1628}\ -\ {125.5024}\ \cdot\ {2.2863}={162.2293}\) General least-squares equation: \(\displaystyle\hat{{{y}}}=\ \alpha\ +\ \beta\ {x}\). Replace \(\displaystyle\alpha\) by \(\displaystyle{a}={162.2293}\) and \(\displaystyle\beta\) by \(\displaystyle{b}={125.5024}\) in the general least-squares equation: \(\displaystyle{y}={a}\ +\ {b}{x}={162.2293}\ +\ {125.5024}{x}\) This regression line is much steeper than the regression line in part (c) and thus the outlier stroungly influences the regression line. f) Let us first determine the necessary sums: \(\displaystyle\sum\ {x}_{{{i}}}={98.31}\)

\(\displaystyle\sum\ {{x}_{{{i}}}^{{{2}}}}={228.4177}\)

\(\displaystyle\sum\ {y}_{{{i}}}={19314}\)

\(\displaystyle\sum\ {x}_{{{i}}}{y}_{{{i}}}={44615.73}\) Next, we can determine \(\displaystyle{S}_{{\times}}\) and \(\displaystyle{S}_{{{x}{y}}}\)

\(\displaystyle{S}_{{\times}}=\ \sum\ {{x}_{{{i}}}^{{{2}}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}^{{{2}}}}}{{{n}}}}={228.4177}\ -\ {\frac{{{98.31}^{{{2}}}}}{{{43}}}}={3.6536}\)

\(\displaystyle{S}_{{{x}{y}}}=\ \sum\ {x}_{{{i}}}{y}_{{{i}}}\ -\ {\frac{{{\left(\sum\ {x}_{{{i}}}\right)}{\left(\sum\ {y}_{{{i}}}\right)}}}{{{n}}}}={44615.73}\ -\ {\frac{{{98.31}\ \cdot\ {19314}}}{{{43}}}}={458.5360}\) The estimate b of the slope \(\displaystyle\beta\) is the ratio of \(\displaystyle{S}_{{{x}{y}}}\) and \(\displaystyle{S}_{{\times}}\): \(\displaystyle{b}=\ {\frac{{{S}_{{{x}{y}}}}}{{{S}_{{\times}}}}}=\ {\frac{{{458.5360}}}{{{3.6536}}}}={125.5024}\) The mean is the sum of all values divided by the number of values: \(\displaystyle\overline{{{x}}}=\ {\frac{{\sum\ {x}_{{{i}}}}}{{{n}}}}=\ {\frac{{{98.31}}}{{{43}}}}={2.2863}\)

\(\displaystyle\overline{{{y}}}=\ {\frac{{\sum\ {y}_{{{i}}}}}{{{n}}}}=\ {\frac{{{19314}}}{{{43}}}}={449.1628}\) The estimate a of the intercept \(\displaystyle\alpha\) is the average of y decreased by the product of the estimate of the slope and the average of x. \(\displaystyle{a}=\ \overline{{{y}}}\ -\ {b}\ \overline{{{x}}}={449.1628}\ -\ {125.5024}\ \cdot\ {2.2863}={162.2293}\) General least-squares equation: \(\displaystyle\hat{{{y}}}=\ \alpha\ +\ \beta\ {x}\). Replace \(\displaystyle\alpha\) by \(\displaystyle{a}={162.2293}\) and \(\displaystyle\beta\) by \(\displaystyle{b}={125.5024}\) in the general least-squares equation: \(\displaystyle{y}={a}\ +\ {b}{x}={162.2293}\ +\ {125.5024}{x}\) This regression line is much steeper than the regression line in part (c) and thus the potential influential observation strongly influences the regression line.