An article in Quality Engineering ("A Comparison of Multi-Response Optimization: Sensitivity to Parameter Selection (1995, Vol. 11, pp. 405-435) conducted a hall replicate of a 25 factorial design to optimize the retort process of beef stew MRES, a military ration. The design factors are sauce viscosity, residual gas, x3 solid liquid ratio, xnet weight, x5 rotation speed. The response variable is the heating rate index, a measure of heat penetration, and there are two replicates. x1 x5 x2 -1 -1 1 1 -1 X3 -1 -1 -1 -1 -1 1 1 -1 Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 -1 1 -1 1 - 1 -1 1 -1 1 - 1 1 1 1 1 1 -1 1 1 -1 -1 1 1 Index Heating Rate 1 8.46 9.61 15.68 14.68 14.94 13.09 12.52 12.71 17.0 16.36 11.44 11.83 10.45 9.22 19.73 16.94 17.37 16.36 14.98 11.93 8.4 8.16 19.08 15.4 13.07 10.55 18.57 20.53 20.59 21 19 14,03 11.31 1 1 -1 -1 1 1 -1 -1 3 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 1 1 1 1 3 1 -1 1 1 -1 1 3 1 26 ) identify the principal generator (2 points) 37 b) Identify the aliases in the model (0.5 point each alias total points) 28 b) Conduct an ANOVA based on the model identified in parts (a) and (b). State your conclusion 29 17 points) 30 Perform post hoc and state your conclusions and recommendations (3 points) 42 Sheet1 An article in Quality Engineering ("A Comparison of Multi-Response Optimization: Sensitivity to Parameter Selection (1995, Vol. 11, pp. 405-435) conducted a hall replicate of a 25 factorial design to optimize the retort process of beef stew MRES, a military ration. The design factors are sauce viscosity, residual gas, x3 solid liquid ratio, xnet weight, x5 rotation speed. The response variable is the heating rate index, a measure of heat penetration, and there are two replicates. x1 x5 x2 -1 -1 1 1 -1 X3 -1 -1 -1 -1 -1 1 1 -1 Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 -1 1 -1 1 - 1 -1 1 -1 1 - 1 1 1 1 1 1 -1 1 1 -1 -1 1 1 Index Heating Rate 1 8.46 9.61 15.68 14.68 14.94 13.09 12.52 12.71 17.0 16.36 11.44 11.83 10.45 9.22 19.73 16.94 17.37 16.36 14.98 11.93 8.4 8.16 19.08 15.4 13.07 10.55 18.57 20.53 20.59 21 19 14,03 11.31 1 1 -1 -1 1 1 -1 -1 3 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 1 1 1 1 3 1 -1 1 1 -1 1 3 1 26 ) identify the principal generator (2 points) 37 b) Identify the aliases in the model (0.5 point each alias total points) 28 b) Conduct an ANOVA based on the model identified in parts (a) and (b). State your conclusion 29 17 points) 30 Perform post hoc and state your conclusions and recommendations (3 points) 42 Sheet1