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Name \rule{3in}{0.01in}\\[0.5in]
Email \rule{3in}{0.01in}

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\Large
Reading Quiz for Sheather Chapter 3
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\item In regression analysis, we say case $i$ has high leverage when $h_{ii}$ is large. 
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\item What does this say about the position of the ordered pair $(x_i,y_i)$ relative to the scatter plot of all of the data $(x_1,y_1), (x_2, y_2), \ldots, (x_n,y_n)$?\\[2in]
\item Are all such ``high leverage'' cases bad?  Why or why not?\\[2in]
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\item{} [based on Ch 3, ex \#1] A business analyst fits the model
\begin{equation}
		\mbox{Fare} = \beta_0 + \beta_1 \mbox{Distance} + \epsilon
		\label{linmod}
\end{equation}
to a data set containing the air fare in dollars and distance in miles to each of 17 U.S. cities from City A.  The regression summary and a couple of plots are given below.
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% Please answer the questions on the back of this sheet.
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% \item Based on the output above, the analyst writes:
% \begin{quote}
% The regression coefficient of the predictor variable, Distance is highly statistically significant and the model explains 99.4\% of the variability in the $Y$-variable, Fare. Thus model (\ref{linmod}) is a highly effective model for both understanding the effects of Distance on Fare and for predicting future values of Fare given the value of the predictor variable, Distance.
% \end{quote}
% Provide a detailed critique of this conclusion, using at least two specific ideas from Chapter 3.


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% \item}

 Does the linear model above provide a good fit?  If yes, give two careful reasons why.  If no, give two careful suggestions for improving the model.  Either way, use specific ideas from Chapter 3.
 
 Use the back of this sheet to provide your answer.

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