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Data for two variables, \( \boldsymbol{x} \) and \( \boldsymbol{y} \), follow.
\begin{tabular}{l|lllll}
\( x_{i} \) & 22 & 24 & 25 & 28 & 41 \\
\hline\( y_{i} \) & 12 & 23 & 31 & 36 & 70
\end{tabular}
a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number.
\( \hat{y}= \) \( \square \)
\( \square \) \( \Theta_{x} \)
b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number.
\begin{tabular}{ccc}
\( x_{i} \) & \( y_{i} \) & \begin{tabular}{c}
Standardized \\
Residual
\end{tabular} \\
\hline 22 & 12 & \( \square \) \\
24 & 23 & \( \square \) \\
25 & 31 & \( \square \) \\
28 & 36 & \( \square \) \\
41 & 70 & \( \square \)
\end{tabular}
Can any of these observations be classified as an outlier? Explain.
- Select your answer - \( \checkmark \)
Because \( \square \) - Select your answer of the standardized residuals are less than -2 or greater than 2 , - Select your answer : \( v \) \( \square \) of the observatons can be classified as an outler.
C. Develop a standardized residual plot for \( \bar{y} \).
A
IStandardized Residual
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