Could you show how to do #34 in Minitab? 9 shows a six-variable RSM design from

Question

Could you show how to do #34 in Minitab?

9 shows a six-variable RSM design from (201 1b). Analyze the response data from TABLE P11.10 The Cotfee Bag Experiment in Problem 11.32 m Viscosity Pressure Plate gap Tear Leaka Kun Center 9 lem 11.31 0 350 18 0.45 0.15 1.8 0.85 0.05 0.35 0.15 186 Factorial 319 Factoria Center 180 17.89 10.07 7.74 400 350 0 0.250.05 Center 0.35 Factorial 319 Factorial380 350 Factorial 380 350 350 Factorial 319 Factorial 319 Factorial380 350 0 1.8 0.15 0.4 0 120.38 0.45 Center 180 0.55 0.2 0 23.56 0 15.24 0 19.91 Axial 1.8 0.05 0.2 186 0.05 ality Progress ("For Starbucks, It's pp. 18-23) describes using a cen- prove the packaging of one-pound roduce an airtight seal that is easy Center 180 11.34. Box and Liu (1999) describe an experiment flying paper helicopters where the objective is to maximize flight time. They used the central composite design shown in Tabl,e P11.11. Each run involved a single helicopter made to the fol lowing specifications: " = wing area (in2),-1 = 11.80 and +1 13.00: = wing-length to width ratio,-1 = 2.25 and +1 = 2.78; x3-base width (in),-1-1.00 and +1 = 1.50; and x4 = base length (in),-1 = 1.50 and +1 = 2.50. Each helicopter was flown four times and the average flight time the top of the coffee bag. The e factors-x plastic viscosity clamp pressure (170-190 psi), 3 mm) and two responses-yi - design is shown in Table P11.10 ure on a scale from 0 to 9 (good portion failing. Each run used a se measurement model for the tear response nodel for the leakage response or both models. Do transforma for either response? If so, refit formed metric and the standard deviation of flight time was recorded. (a) Fit a second-order model to the average flight time response (b) Fit a second-order model to the standard deviation of flight time response ace plots and contour plots for e interpretations for the fitted (c) Analyze the residuals for both models from parts (a) and (b). Are transformations on the response(s) neces- sary? If so, fit the appropriate models you recommend for process kage and keep tear below 0.75? (d) What design would you recommend to maximize the flight time? (e) What design would you recommend to maximize the flight time while simultaneously minimizing the stan dard deviation of fight time?

Explanation / Answer

Design Expert Output

Response 1                        Avg Fit Time

ANOVA for response surface reduced quadartic model

Analysis of variance table [Partial sum of squares - Type III]

P-value

Prob >F

Std. dev 0.040           R squared    0.9060

mean   3.66                Adj R-squared 0.8486

c.v% 1.09                    Pred R squared 0.7581

press 0.074                   Adeq predision   19.913

Source Sum of squares df Mean square F value

P-value

Prob >F

Model 0.28 11 0.025 15.78 <0.0001 significance A-wing area 1.67E005 1 1.667E005 0.010 0.9198 B length width 0.062 1 0.062 38.83 <0.0001 Cbase width 1.500E004 1 1.500E004 0.094 0.7628 Dbase width 0.089 1 0.089 55.61 <0.0001 AB 0.013 1 0.013 8.28 0.0100 AC 0.023 1 0.023 14.09 0.0015 AD 0.031 1 0.031 19.17 0.0004 BC 0.034 1 0.034 21.43 0.0002 CD 7.225E003 1 7.22E003 4.52 0.0475 A2 7.511E003 1 7.51E003 4.70 0.0438 C2 0.013 1 0.013 8.04 0.0110 Residual 0.029 18 1.597E003 LACK OF FIT 0.020 13 1.513E003 0.83 0.6382 PURE ERROR 9.083E003 5 COR TOTAL 0.31 29

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