Question

Harry and Judy Peterson-Nedry (two friends of the author) own a vineyard and winery in Newberg, Oregon. They grow several varieties of grapes and manufacture wine. Harry and Judy have used factorial designs for process and product development in the winemaking segment of their business. This problem describes the experiment conducted for their 1985 Pinot Noir. Eight variables, shown in Table P8.5, were originally studied in this experiment: TABLE P8.5 Factors and Levels for the Winemaking Experiment $$ \begin{array}{lll} \text { Variable } & \text { Low Level }(-) & \text { High Level }(+) \\ \hline A=\text { Pinot Noir clone } & \text { Pommard } & \text { Wadenswil } \\ B=\text { Oak type } & \text { Allier } & \text { Troncais } \\ C=\text { Age of barrel } & \text { Old } & \text { New } \\ D=\text { Yeastskin contact } & \text { Champagne } & \text { Montrachet } \\ E=\text { Stems } & \text { None } & \text { All } \\ F=\text { Barrel toast } & \text { Light } & \text { Medium } \\ G=\text { Whole cluster } & \text { None } & 10 \% \\ H=\text { Fermentation temperature } & \text { Low }\left(75^{\circ} \mathrm{F} \mathrm{max}\right) & \text { High }\left(92^{\circ} \mathrm{F} \text { max }\right) \end{array} $$ Harry and Judy decided to use a $2 \mathrm{Iv}_{\mathrm{NV}}^{\mathrm{g}-4}$ design with 16 runs. The wine was tastetested by a panel of experts on March 8, 1986. Each expert ranked the 16 samples of wine tasted, with rank I being the best. The design and the taste-test panel results are shown in Table P8.6. (a) What are the alias relationstips in the design selected by Harry and Judy? (b) Use the average ranks (y) as a response variable. Analyze the data and draw conclusions. You will find it helpful to examine a normal probability plot of the effect estimates. (c) Use the standard deviation of the ranks (or some appropriate transformation such as $\log s$ ) as a response variable. What conclusions can you draw about the effects of the eight variables on variability in wine quality? (d) After looking at the results, Harry and Judy decide that one of the panel members (DCM) knows more about beer than he does about wine. so they decide to delete his ranking. What effect would this have on the results and conclusions from parts (b) and (c)? (e) Suppose that just before the start of the experiment, Harry and Judy discovered that the eight new barrels they ordered from France for use in the experiment would not anive in time. and ail 16 nuns would have to be made with old barrels. If Harry and Judy just drop column $C$ from their design, what does this do to the alias relationships? Do they need to start over and construct a new design? (f) Hanry and Judy know from experience that some treatment combinations are unlikely to produce good results. For example, the run with all eight variables at the high level generally results in a poodly rated wine. This was confirmed in the March 8. 1986 taste test. They want to set up a new design for their 1986 Pinot Noir using these same eight variables. but they do not want to make the run with all eight factors at the high level. What design would you suggest?

   Harry and Judy Peterson-Nedry (two friends of the author) own a vineyard and winery in Newberg, Oregon. They grow several varieties of grapes and manufacture wine. Harry and Judy have used factorial designs for process and product development in the winemaking segment of their business. This problem describes the experiment conducted for their 1985 Pinot Noir. Eight variables, shown in Table P8.5, were originally studied in this experiment:
TABLE P8.5
Factors and Levels for the Winemaking Experiment
$$
\begin{array}{lll}
\text { Variable } & \text { Low Level }(-) & \text { High Level }(+) \\
\hline A=\text { Pinot Noir clone } & \text { Pommard } & \text { Wadenswil } \\
B=\text { Oak type } & \text { Allier } & \text { Troncais } \\
C=\text { Age of barrel } & \text { Old } & \text { New } \\
D=\text { Yeastskin contact } & \text { Champagne } & \text { Montrachet } \\
E=\text { Stems } & \text { None } & \text { All } \\
F=\text { Barrel toast } & \text { Light } & \text { Medium } \\
G=\text { Whole cluster } & \text { None } & 10 \% \\
H=\text { Fermentation temperature } & \text { Low }\left(75^{\circ} \mathrm{F} \mathrm{max}\right) & \text { High }\left(92^{\circ} \mathrm{F} \text { max }\right)
\end{array}
$$
Harry and Judy decided to use a $2 \mathrm{Iv}_{\mathrm{NV}}^{\mathrm{g}-4}$ design with 16 runs. The wine was tastetested by a panel of experts on March 8, 1986. Each expert ranked the 16 samples of wine tasted, with rank I being the best. The design and the taste-test panel results are shown in Table P8.6.
(a) What are the alias relationstips in the design selected by Harry and Judy?
(b) Use the average ranks (y) as a response variable. Analyze the data and draw conclusions. You will find it helpful to examine a normal probability plot of the effect estimates.
(c) Use the standard deviation of the ranks (or some appropriate transformation such as $\log s$ ) as a response variable. What conclusions can you draw about the effects of the eight variables on variability in wine quality?
(d) After looking at the results, Harry and Judy decide that one of the panel members (DCM) knows more about beer than he does about wine. so they decide to delete his ranking. What effect would this have on the results and conclusions from parts (b) and (c)?
(e) Suppose that just before the start of the experiment, Harry and Judy discovered that the eight new barrels they ordered from France for use in the experiment would not anive in time. and ail 16 nuns would have to be made with old barrels. If Harry and Judy just drop column $C$ from their design, what does this do to the alias relationships? Do they need to start over and construct a new design?
(f) Hanry and Judy know from experience that some treatment combinations are unlikely to produce good results. For example, the run with all eight variables at the high level generally results in a poodly rated wine. This was confirmed in the March 8. 1986 taste test. They want to set up a new design for their 1986 Pinot Noir using these same eight variables. but they do not want to make the run with all eight factors at the high level. What design would you suggest?
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Design and Analysis of Experiments
Design and Analysis of Experiments
Douglas C.… 7th Edition
Chapter 8, Problem 27 ↓

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To determine the alias relationships in a \(2^{8-4}\) fractional factorial design, we need to identify the generators of the design. In a \(2^{8-4}\) design, we have 8 factors and we are using 4 degrees of freedom, which means we will have \(2^4 = 16\) runs. The  Show more…

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Harry and Judy Peterson-Nedry (two friends of the author) own a vineyard and winery in Newberg, Oregon. They grow several varieties of grapes and manufacture wine. Harry and Judy have used factorial designs for process and product development in the winemaking segment of their business. This problem describes the experiment conducted for their 1985 Pinot Noir. Eight variables, shown in Table P8.5, were originally studied in this experiment: TABLE P8.5 Factors and Levels for the Winemaking Experiment $$ \begin{array}{lll} \text { Variable } & \text { Low Level }(-) & \text { High Level }(+) \\ \hline A=\text { Pinot Noir clone } & \text { Pommard } & \text { Wadenswil } \\ B=\text { Oak type } & \text { Allier } & \text { Troncais } \\ C=\text { Age of barrel } & \text { Old } & \text { New } \\ D=\text { Yeastskin contact } & \text { Champagne } & \text { Montrachet } \\ E=\text { Stems } & \text { None } & \text { All } \\ F=\text { Barrel toast } & \text { Light } & \text { Medium } \\ G=\text { Whole cluster } & \text { None } & 10 \% \\ H=\text { Fermentation temperature } & \text { Low }\left(75^{\circ} \mathrm{F} \mathrm{max}\right) & \text { High }\left(92^{\circ} \mathrm{F} \text { max }\right) \end{array} $$ Harry and Judy decided to use a $2 \mathrm{Iv}_{\mathrm{NV}}^{\mathrm{g}-4}$ design with 16 runs. The wine was tastetested by a panel of experts on March 8, 1986. Each expert ranked the 16 samples of wine tasted, with rank I being the best. The design and the taste-test panel results are shown in Table P8.6. (a) What are the alias relationstips in the design selected by Harry and Judy? (b) Use the average ranks (y) as a response variable. Analyze the data and draw conclusions. You will find it helpful to examine a normal probability plot of the effect estimates. (c) Use the standard deviation of the ranks (or some appropriate transformation such as $\log s$ ) as a response variable. What conclusions can you draw about the effects of the eight variables on variability in wine quality? (d) After looking at the results, Harry and Judy decide that one of the panel members (DCM) knows more about beer than he does about wine. so they decide to delete his ranking. What effect would this have on the results and conclusions from parts (b) and (c)? (e) Suppose that just before the start of the experiment, Harry and Judy discovered that the eight new barrels they ordered from France for use in the experiment would not anive in time. and ail 16 nuns would have to be made with old barrels. If Harry and Judy just drop column $C$ from their design, what does this do to the alias relationships? Do they need to start over and construct a new design? (f) Hanry and Judy know from experience that some treatment combinations are unlikely to produce good results. For example, the run with all eight variables at the high level generally results in a poodly rated wine. This was confirmed in the March 8. 1986 taste test. They want to set up a new design for their 1986 Pinot Noir using these same eight variables. but they do not want to make the run with all eight factors at the high level. What design would you suggest?
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Key Concepts

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Fractional Factorial Design
A fractional factorial design is an experimental approach used to study multiple factors using only a fraction of the runs required for a full factorial design. It enables researchers to estimate main effects and selected interactions efficiently, especially when the number of factors is large. However, some effects become confounded or aliased with others, meaning they cannot be estimated independently.
Alias Relationships
Alias relationships occur in fractional factorial designs when two or more effects are confounded due to the reduced number of experimental runs. This means that the effects are intertwined, and one cannot tell from the experimental data alone which effect is responsible for the observed outcome. Understanding these relationships is essential for correctly interpreting the results and planning further experiments.
Effect Estimation and Evaluation
Effect estimation is the process of quantifying the impact of each experimental factor on the response variable. This evaluation often involves calculating main effects and interactions, then using tools like normal probability plots to distinguish significant effects from random noise. These plots help assess whether the estimated effects deviate from what would be expected under normal randomness.
Response Transformation for Variability Analysis
Response transformation, such as taking the logarithm of the standard deviation of the response, is used in experiments to stabilize variance across treatment combinations or to meet the assumptions of statistical analysis. This approach provides a more reliable assessment of how experimental factors influence variability, complementing analyses focused on mean responses.
Design Modification and Robustness
Modifying an experimental design—due to issues such as loss of data or constraints on certain treatment combinations—requires careful evaluation of its impact on aliasing, effect estimates, and overall conclusions. Robustness in design means that even with modifications, the experimental results remain valid and interpretable, highlighting the importance of strategic planning and adaptation in design.

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An article in the Journal of Quality Technology (Vol. 17, 1985, pp. 198-206) describes the use of a replicated fractional factorial to investigate the effect of five factors on the free height of leaf springs used in an automotive application. The factors are A = furnace temperature, B = heating time, C = transfer time, D = hold down time, and E = quench oil temperature. The data are shown below: A B C D E Free Height, y - - - - - 7.78, 7.78, 7.81 + - - + - 8.15, 8.18, 7.88 - + - + - 7.50, 7.56, 7.50 + + - - - 7.59, 7.56, 7.75 - - + + - 7.54, 8.00, 7.88 + - + - - 7.69, 8.09, 8.06 - + + - - 7.56, 7.52, 7.44 + + + + - 7.56, 7.81, 7.69 - - - - + 7.50, 7.25, 7.12 + - - + + 7.88, 7.88, 7.44 - + - + + 7.50, 7.56, 7.50 + + - - + 7.63, 7.75, 7.56 - - + + + 7.32, 7.44, 7.44 + - + - + 7.56, 7.69, 7.62 - + + - + 7.18, 7.18, 7.25 + + + + + 7.81, 7.50, 7.59 (a) Write out the alias structure for this design. What is the resolution of this design? (b) Analyze the data. What factors influence the mean free height? (c) Calculate the range and standard deviation of free height for each run. Is there any indication that any of these factors affects variability in free height? (d) Analyze the residuals from this experiment and comment on your findings. (e) Is this the best possible design for five factors in 16 runs? Specifically, can you find a fractional factorial design for five factors in 16 runs with higher resolution than this one?

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