\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{RESULTS-With Relevancy Filtering}}}
    
    \large{
      \begin{minipage}{13 cm}
          \begin{center}
            {\small{
            \vspace{3mm}
            Nasa93 Results
			 \include{nasa93}
            }}
          \end{center}
      \end{minipage}
  
  \vspace{3 mm}
Figure above, shows the the win-tie-loss values for the subsets of Nasa93.
This result  shows us that, in all three treatments, the $tie$ values are quite high.
This indicates that, for 
at least 75\% of the tests, there is no statistical difference between filtered {\em cross} and {\em within} results.

    }

    \vspace{3mm}
    \begin{minipage}{13 cm}
          \begin{center}
            {\small{
            Cocomo81 Results
			 \include{cocomo81}
            }}
          \end{center}
      \end{minipage}
      
      \vspace{3mm}
    Above Figure shows the win-tie-loss values for the subsets of Cocomo81.
In two out of the three treatments the $tie$ values are 19, which tells us that for these treatments, {\em within} and {\em cross} performance
are almost identical.
However, the first treatment shows a preference for {\em within} data on thirteen of the twenty tests.

\vspace{3mm}
\begin{minipage}{13 cm}
          \begin{center}
            {\small{
            Desharnais Results
			\include{desharnais}
            }}
          \end{center}
      \end{minipage}
      
      \vspace{3mm}
The win-tie-loss values for subsets of Desharnais are given in the Figure above.
Two out of the three treatments show identical $tie$ values of 19, which again suggests that the performance of filtered {\em cross} datasets is
statistically identical to {\em within} datasets.

\vspace{3 mm}
    In the majority case
($\frac{7}{9}$ treatments) the {\em cross} data performs as well as the
{\em within} data for effort estimation.  There are only two treatments,
DesL3 and Coc81o, where {\em within} performance was significantly better
than {\em cross} performance, whose explanation can be hidden in the dataset size, but with currently-available information
it is difficult to suggest any conclusive reason for that situation.
    \vspace{-0.1em}
\end{kasten}


%\begin{kasten}
%    \section*{ \hspace{0.1cm} {\color{red} \underline{FOR MORE INFORMATION}}}
%    \vspace{-0.5em}
%    \normalsize{
%      Ekrem Kocaguneli (\url{ekocagun@mix.wvu.edu})\\
%      Tim Menzies Ph.D. (\url{tim@menzies.us})\\
%    }
%    \vspace{-0.5em}
%\end{kasten}

\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{SUPPORT}}}
    \vspace{-0.5em}
    \large{
      \begin{minipage}{2cm}
        \includegraphics[width=2cm]{nsf_logo.eps}
      \end{minipage}
      \begin{minipage}{11cm}
This research was carried out at West Virginia
University under a grant from the National Science Foundation (NSF CISE 71608561).
      \end{minipage}
    }
\end{kasten}
