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\begin{figure*}[th!]
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  \begin{tabular}{l | l  l  l | c  c  c }   
  \textbf{Data set} &\textbf{Train Set} & \textbf{Test Set} & \textbf{Method} & \textbf{Win} & \textbf{Tie} & \textbf{Loss} \\ \hline			
Nasa93	&	Nasa93c1	&	Leave-one-out test instance	&	Within-Company	& 1	&   0	& 0 \\
	&	Nasa93c2 and Nasa93c5	&	Nasa93c1	&	Cross-Company			& 0	&   0	& 1	\\
	&	Nasa93c2	&	Leave-one-out test instance	&	Within-Company		& 1	&	0	& 0 \\
	&	Nasa93c1 and Nasa93c5	&	Nasa93c2	&	Cross-Company			& 0	&	0	& 1	\\
	&	Nasa93c5	&	Leave-one-out test instance	&	Within-Company		& 0	&	1	& 0	\\
    &	Nasa93c1 and Nasa93c2	&	Nasa93c5	&	Cross-Company			& 0	&	1	& 0	\\	\hline
Cocomo81	&	Coc81o	&	Leave-one-out test instance	&	Within-Company	& 1 &	0	& 0	\\	
	&	Coc81e and Coc81s	&	Coc81o	&	Cross-Company					& 0	&	0	& 1	\\
	&	Coc81e	&	Leave-one-out test instance	&	Within-Company			& 0	&	1	& 0 \\
	&	Coc81o and Coc81s	&	Coc81e	&	Cross-Company					& 0	&	1	& 0	\\
	&	Coc81s	&	Leave-one-out test instance	&	Within-Company			& 0 &	1	& 0	\\
	&	Coc81o and Coc81e	&	Coc81s	&	Cross-Company					& 0	&	1	& 0\\	\hline
Desharnais	&	DesL1	&	Leave-one-out test instance	&	Within-Company	& 1	&	0	& 0	\\
	&	DesL2 and DesL3	&	DesL1	&	Cross-Company						& 0	&	0	& 1	\\
	&	DesL2	&	Leave-one-out test instance	&	Within-Company			& 0	&	1	& 0	\\	
	&	DesL1 and DesL3	&	DesL2	&	Cross-Company						& 0	&	1	& 0	\\
	&	DesL3	&	Leave-one-out test instance	&	Within-Company			& 1 &	0	& 0	\\
	&	DesL1 and DesL2	&	DesL3	&	Cross-Company						& 0	&	0	& 1	\\
	\end{tabular}
\caption{MRE win-tie-loss results without relevancy filtering.
In each treatment, there is a within and a cross-company experiment. In cross-company experiment, a linear regression model is built on cross-company data and tested on within-company. In within-company experiment, test instance is selected with leave-one-out, a linear regression model is built on the remaining instances and tested on the selected test instance.}\label{fig:wltNoFiltering}
\end{figure*}

In our research we also observe the effect of lack of relevancy filtering on within and cross-company dataset performances. 
In lack of relevancy filtering we have 9 treatments like previous experiments and for each treatment we observe the estimation performances when within-company and cross-company datasets are used. 
For that purpose we use a linear regression model.
In cross-company experiments we select one of the 3 subsets as the test set and the remaining two as the train set.
We then build a linar regression model on the cross-company data and apply this model on the test set.
For the within-company dataset we also use a linear regression model.
The test case selection for within-company experiment is performed in accordance with leave-one-out method, which picks up one of the instances in the dataset as the test set and uses the reamining instances as the train set.
The linear regression model that is built on the train set is then tested on the single test instance.

After applying linear regression model on within and cross-company datasets, we calculate the win-tie-loss values for each treatment.
The win-tie-loss values in the absence of relevancy filtering are given in Figure \ref{fig:wltNoFiltering}.
One caution before commenting on the results is that in Figure \ref{fig:wltNoFiltering} the sum of win-tie-loss values do not add up to 20 unlike previous cases.
The reason is that we can make only a single run in that scenario, since we will end up with the same linear model in different runs.
When we look at the win-tie-loss results in Figure \ref{fig:wltNoFiltering}, we see that 4 treatments (Nasa93c5, Coc81e, Coc81s and DesL2) have tie values of 1.
In the remaining 5 treatments the within-company datasets have win values of 1, i.e. produces lower MRE values when compared to cross-company datasets.
Therefore, in the absence of relevancy filtering, the within-company datasets yield significantly lower MRE values in majority of the cases.

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