5. An experiment will be performed to study the amount of waste material left over from a chemical process as a function of temperature, pH, and cooling rate. The study variables (and their levels) are: Description Variable -1 +1 Units Temperature Temp 60 80 °C pH Cooling Rate рН 6 7.2 ΝΑ Rate 2 5 °C/minute The experiment will use a 23 full factorial design and will be blocked on replicates. MM&B Inc. a. The nominal waste response value is expected to be about 800 lb and the standard deviation of the process noise is about 9 lb. A waste reduction of 15 lb would be a practically significant effect. Calculate the number of replicates required to detect a 15 lb effect with 90% power. b. Use MINITAB's Stat> DOE> Factorial> Create Factorial Design> menu to create the experimental design worksheet with the number of replicates calculated in part a. Use the variable name and variable level values exactly as specified in the table above. Remember to set up blocking on replcates. c. Run the Waste.mtb macro from the File> Run an Exec menu to generate the simulated response. d. Use the Stat> DOE> Factorial> Analyze Factorial Design> menu. Fit the full model and then apply Occam to refine the model. e. Use the Stat> DOE> Factorial> Factorial Plots menu to create the main effect and interaction plots. Interpret the plots. f. Use the Stat> DOE> Factorial> Response Optimizer menu to determine the process variable settings that will minimize the response.
5. An experiment will be performed to study the amount of waste material left over from a chemical process as a function of temperature, pH, and cooling rate. The study variables (and their levels) are: Description Variable -1 +1 Units Temperature Temp 60 80 °C pH Cooling Rate рН 6 7.2 ΝΑ Rate 2 5 °C/minute The experiment will use a 23 full factorial design and will be blocked on replicates. MM&B Inc. a. The nominal waste response value is expected to be about 800 lb and the standard deviation of the process noise is about 9 lb. A waste reduction of 15 lb would be a practically significant effect. Calculate the number of replicates required to detect a 15 lb effect with 90% power. b. Use MINITAB's Stat> DOE> Factorial> Create Factorial Design> menu to create the experimental design worksheet with the number of replicates calculated in part a. Use the variable name and variable level values exactly as specified in the table above. Remember to set up blocking on replcates. c. Run the Waste.mtb macro from the File> Run an Exec menu to generate the simulated response. d. Use the Stat> DOE> Factorial> Analyze Factorial Design> menu. Fit the full model and then apply Occam to refine the model. e. Use the Stat> DOE> Factorial> Factorial Plots menu to create the main effect and interaction plots. Interpret the plots. f. Use the Stat> DOE> Factorial> Response Optimizer menu to determine the process variable settings that will minimize the response.
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 91E
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Transcribed Image Text:5. An experiment will be performed to study the amount of waste material left over from a chemical
process as a function of temperature, pH, and cooling rate. The study variables (and their levels) are:
Description Variable -1 +1
Units
Temperature Temp 60 80
°C
pH
Cooling Rate
рН 6 7.2
ΝΑ
Rate 2
5
°C/minute
The experiment will use a 23 full factorial design and will be blocked on replicates.
MM&B Inc.
a. The nominal waste response value is expected to be about 800 lb and the standard deviation of the
process noise is about 9 lb. A waste reduction of 15 lb would be a practically significant effect.
Calculate the number of replicates required to detect a 15 lb effect with 90% power.
b. Use MINITAB's Stat> DOE> Factorial> Create Factorial Design> menu to create the
experimental design worksheet with the number of replicates calculated in part a. Use the variable
name and variable level values exactly as specified in the table above. Remember to set up
blocking on replcates.
c. Run the Waste.mtb macro from the File> Run an Exec menu to generate the simulated response.
d. Use the Stat> DOE> Factorial> Analyze Factorial Design> menu. Fit the full model and then
apply Occam to refine the model.
e. Use the Stat> DOE> Factorial> Factorial Plots menu to create the main effect and interaction
plots. Interpret the plots.
f. Use the Stat> DOE> Factorial> Response Optimizer menu to determine the process variable
settings that will minimize the
response.
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