ADD jeff wu's epic textbook

tex/references.bib

@@ -2451,6 +2451,18 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
   publisher = {Wiley},
 }
 
+@Book{Wu2009,
+  author    = {Wu, Hamada},
+  publisher = {John Wiley \& Sons},
+  title     = {Experiments 2e},
+  year      = {2009},
+  isbn      = {0471699462},
+  month     = jul,
+  ean       = {9780471699460},
+  pagetotal = {756},
+  url       = {https://www.ebook.de/de/product/8324352/wu\_hamada\_experiments\_2e.html},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

tex/thesis.tex

@@ -1033,10 +1033,10 @@ described\cite{Budagian2004}.
 The \gls{dms} system has a number of parameters that can be optimized, and a
 \gls{doe} is an ideal framework to test multiple parameters simultaneously. The
 goal of \gls{doe} is to answer a data-driven question with the least number of
-resources. It was developed in many non-biological industries throughout the
-\nth{20} century such as the automotive and semiconductor industries where
-engineers needed to minimize downtime and resource consumption on full-scale
-production lines.
+resources\cite{Wu2009}. It was developed in many non-biological industries
+throughout the \nth{20} century such as the automotive and semiconductor
+industries where engineers needed to minimize downtime and resource consumption
+on full-scale production lines.
 
 At its core, a \gls{doe} is simply a matrix of conditions to test where each row
 is usually called a `run' and corresponds to one experimental unit to which the
@@ -1055,7 +1055,6 @@ possible. While there are many types of \glspl{doe} depending on the nature
 of the parameters and the goal of the experimenter, they all share common
 principles:
 
-% BACKGROUND cite wu hamada... because I feel like it
 \begin{description}
 \item [randomization --] The order in which the runs are performed should
   ideally be as random as possible. This is to mitigate against any confounding