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{\Large Advanced Data Analysis, II}
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36-402 \hfill Homework 2 \hfill Spring, 1996
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Homework due --- In class, Thursday February 22.
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The following problems are taken from the book ``Applied Linear
Statistical Models'' by Neter, Wasserman, and Kutner.
The data are available in the directory {\tt /afs/andrew/hss/stat/data/402}.
\begin{enumerate}
\item {\bf Eye contact effect.}  In a study of the effect of eye
  contact (factor $A$) and gender of personnel officer(factor $B$) on
  a personnel officer's assessment of likely job success of an
  application, 10 male and 10 female personnel officers were shown a
  frontview photograph of an applicant's face and were asked to give
  the person in the photograph a success rating on a scale of 0 (tot
  failure) to 20 (outstanding success).  Half of the officers in each
  gender group were chosen at random to receive a version of the
  photograph in which the subject made eye contact with the camera
  lens.  The other half received a version in which there was no eye
  contact.  The data are in {\tt eyecontact.dat}
The success ratings follow.
  \begin{center}
  \begin{tabular}{crr}
    &\multicolumn{2}{c}{{\em Gender of officer\/}} \\
    {\em Eye contact\/}  & {\em Male\/} & {\em Female\/} \\ \hline
    Present & 11 & 15 \\
    & 7 & 12 \\
    & 12 & 14 \\
    & 6 & 11 \\
    & 10 & 16 \\
    \\
    Absent & 12 & 14 \\
    & 16 & 17 \\
    & 10 & 13 \\
    & 13 & 20 \\
    & 14 & 18 
  \end{tabular}
  \end{center}

  \begin{enumerate}
  \item Analyze the data, looking at fitted values, residuals, the
    ANOVA table, and assumptions.
  \item Who would you rather have interview you?  Is Eye contact
    important? 
  \end{enumerate}
  
\item {\bf Hay fever relief.}  
  A research laboratory was developing a new compound for the relief
  of severe case of hat fever.  In an experiment with 36 volunteers,
  the amounts of the two active ingredients in the compound were
  varied at three levels each.  Randomization was used in assigning
  four volunteers to each of the nine treatments.  The data on hours
  of relief follow, and are in {\tt hayfever.dat}

  \begin{center}
    \begin{tabular}{lrrr}
      &\multicolumn{3}{c}{{\em Ingredient 2\/}}\\
      {\em Ingredient 1\/} & {\em Low\/}& {\em Medium\/} & {\em High\/}\\\hline
      Low &2.4& 4.6& 4.8\\
     &2.7& 4.2& 4.5\\
     &2.3& 4.9& 4.4\\
     &2.5& 4.7& 4.6\\
     \\
     Medium&5.8& 8.9& 9.1\\
     &5.2& 9.1& 9.3\\
     &5.5& 8.7& 8.7\\
     &5.3& 9.0& 9.4\\
     \\
     High&6.1& 9.9& 13.5\\
     &5.7& 10.5& 13.0\\
     &5.9& 10.6& 13.3\\
     &6.2& 10.1& 13.2\\
    \end{tabular}
  \end{center}


  \begin{enumerate}
  \item Analyze the data, looking at fitted values, residuals, the
    ANOVA table, and assumptions.
  \item Who would you rather have interview you?  Is Eye contact
    important? 
  \item Summarize the relationship between the factor levels and the
    response. 
  \item What factor level(s) is/are best?
  \end{enumerate}
  
\item {\bf Disk Drive Service.}
  The staff of a service center for electronic equipment includes
  three technicians who specialize in repairing three widely used
  makes of disk drives for desk-top computers.  It was desired to
  study the effects of technician and make of disk drive on the
  service time.  The data that follow show the number of minutes
  required to complete the repair job in a study where each technician
  was randomly assigned to five jobs on each make of disk drive. The
  data are in {\tt diskdrive.dat}
  \begin{center}
    \begin{tabular}{lrrr}
      &\multicolumn{3}{c}{{\em Make of Drive\/}}\\
      {\em Technician\/} & {\em Make 1\/}& {\em Make 2\/} & {\em Make 3\/}\\\hline
      Technician 1&62& 57& 59\\
      &48& 45& 53\\
      &63& 39& 67\\
      &57& 54& 66\\
      &69& 44& 47\\
      \\
      Technician 2&51& 61& 55\\
      &57& 58& 58\\
      &45& 70& 50\\
      &50& 66& 69\\
      &39& 51& 49\\
      \\
      Technician 3&59& 58& 47\\
      &65& 63& 56\\
      &55& 70& 51\\
      &52& 53& 44\\
      &70& 60& 50\\
    \end{tabular}
  \end{center}
  \begin{enumerate}
  \item Analyze the data, looking at fitted values, residuals, the
    ANOVA table, and assumptions.
  \item Who would you rather have repair your hardware?  Does it
    matter what type of drive you have?  Is there an interaction?
  \end{enumerate}
  

\end{enumerate}
 Let me know how things are going!
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