use these results  to set business goals 

large organizations in dynamic envirouments have data colelction chalalnges.

data minign possible

goals have to be tuned to the available data

value of the comprmised goals must be carefully assessed.

inasa has ben under-estimating the error potential for its software since, at least 2005.

silap model good: ra paras

silap modeling poor: devired params

rule elarnes such as ripper can learn succinct theoires of coplex
business situations.in this case, our elarnes perfromed increbily
well (f=0.95). normally we would adocated use other methods (see
cocomo and data mining paper). but in this case, the performacen
results were so good that owe are ot strongly m,oticvated to explore
other results.

empiricl methods have to been tuned to the availabel data. lesson
1 of SEL. in very dunamic oranizations like NASA, the stability
required for SEL-style experienc factories is difficult to acheive.

iv\&v valbale: \fig{issues}
cost to fix. relative value of tasks.

too much proisng tasks, too little assessing of them. 
hopefully, this study will motivate others to perform more of this kind of analysis.

methods by which production is controlled on a natioanl elvel vey
differnt to how prodction is controlled in small labs.

fss ok to throw away much of the data.

the world is cross-correlated.
things that look poor in isolation work well in siolation caution gainst the single spreadshhet
plot where one feature is plotted against the target variable

se complex, but not som complex that we can't find patterns

se characteriztes but much theory and very little experimental verification. 
