%   THIS IS DATAPLOT DATA FILE BOXCAKE.DAT
%   FLOUR/SHORTENING/EGGS/TEMP/TIME EFFECT ON CAKE TASTE
%   EXAMPLE OF TAGUCHI PARAMETER DESIGN
%   REFERENCE--BOX, G.E.P, AND JONES, XX.
%              QUALITY PROGRESS, JUNE 1987, PAGE 56.
%   REFERENCE--BOX, G.E.P., BISGAARD, SOREN, & FUNG, CONRAD ,
%              "AN EXPLANATION AND CRITIQUE OF TAGUCHI'S
%              CONTRIBUTIONS TO QUALITY ENGINEERING",
%              QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL,
%              VOLUME 4, 1988, PAGES 123-131.
%
%   NUMBER OF OBSERVATIONS = 32 + 5 = 37
%   TOTAL NUMBER OF VARIABLES = 6
%
%   Description: Introduce a new cake mix which is robust to inaccurate
%   setting of ofen temperature and baking time.
%
%   RESPONSE VARIABLE = TASTE RATING OF CAKE (0 = BAD, 7 = GOOD)
%   FACTOR 1 = FLOUR (2 LEVELS-- )                control variable
%   FACTOR 2 = SHORTENING (2 LEVELS--)            control variable
%   FACTOR 3 = EGGS (2 LEVELS--)                  convrol variable
%   FACTOR 4 = OVEN TEMPERATURE (2 LEVELS--)      noise variable
%   FACTOR 5 = OVEN TIME (2 LEVELS--)             noise variable
%
%   TASTE   FLOUR  SHORT. EGGS   TEMP.  TIME
%   -----------------------------------------
Data = ...
   [6.7      0      0      0      0      0; ...
    3.4      0      0      0     -1     -1; ...
    5.4      0      0      0     +1     -1; ...
    4.1      0      0      0     -1     +1; ...
    3.8      0      0      0     +1     +1; ...
    1.1     -1     -1     -1     -1     -1; ...
    3.8      1     -1     -1     -1     -1; ...
    3.7     -1      1     -1     -1     -1; ...
    4.5      1      1     -1     -1     -1; ...
    4.2     -1     -1      1     -1     -1; ...
    5.0      1     -1      1     -1     -1; ...
    3.1     -1      1      1     -1     -1; ...
    3.9      1      1      1     -1     -1; ...
    5.7     -1     -1     -1      1     -1; ...
    4.9      1     -1     -1      1     -1; ...
    5.1     -1      1     -1      1     -1; ...
    6.4      1      1     -1      1     -1; ...
    6.8     -1     -1      1      1     -1; ...
    6.0      1     -1      1      1     -1; ...
    6.3     -1      1      1      1     -1; ...
    5.5      1      1      1      1     -1; ...
    6.4     -1     -1     -1     -1      1; ...
    4.3      1     -1     -1     -1      1; ...
    6.7     -1      1     -1     -1      1; ...
    5.8      1      1     -1     -1      1; ...
    6.5     -1     -1      1     -1      1; ...
    5.9      1     -1      1     -1      1; ...
    6.4     -1      1      1     -1      1; ...
    5.0      1      1      1     -1      1; ...
    1.3     -1     -1     -1      1      1; ...
    2.1      1     -1     -1      1      1; ...
    2.9     -1      1     -1      1      1; ...
    5.2      1      1     -1      1      1; ...
    3.5     -1     -1      1      1      1; ...
    5.7      1     -1      1      1      1; ...
    3.0     -1      1      1      1      1; ...
    5.4      1      1      1      1      1];

D = 5;                          % Data dimension
DCon = 3; DEnv = D-DCon;        % number of control and noise variables
DataY = Data(:,1);              % output variable
DataX = Data(:,2:end);          % all input variables
DataXCon = DataX(:,1:DCon);     % control input variables
DataXEnv = DataX(:,DCon+1:end); % noise input variables
LB = 2*[-1,-1,-1,-1,-1];
UB = 2*[1,1,1,1,1];
bound = [LB',UB'];

