Yates analysis

Before performing a Yates analysis, the data should be arranged in "Yates order". That is, given k factors, the k^th column consists of 2^{(k - 1)} minus signs (i.e., the low level of the factor) followed by 2^{(k - 1)} plus signs (i.e., the high level of the factor). For example, for a full factorial design with three factors, the design matrix is

${\displaystyle ---}$

${\displaystyle +--}$

${\displaystyle -+-}$

${\displaystyle ++-}$

${\displaystyle --+}$

${\displaystyle +-+}$

${\displaystyle -++}$

${\displaystyle +++}$

Determining the Yates order for fractional factorial designs requires knowledge of the confounding structure of the fractional factorial design.

A Yates analysis generates the following output.

• A factor identifier (from Yates order). The specific identifier will vary depending on the program used to generate the Yates analysis. Dataplot, for example, uses the following for a 3-factor model.

1 = factor 1

2 = factor 2

3 = factor 3

12 = interaction of factor 1 and factor 2

13 = interaction of factor 1 and factor 3

23 = interaction of factor 2 and factor 3

123 = interaction of factors 1, 2, and 3

• A ranked list of important factors. That is, least squares estimated factor effects ordered from largest in magnitude (most significant) to smallest in magnitude (least significant).
• A t-value for the individual factor effect estimates. The t-value is computed as

${\displaystyle t={\frac {e}{s_{e}}}}$

where e is the estimated factor effect and s_e is the standard deviation of the estimated factor effect.

• The residual standard deviation that results from the model with the single term only. That is, the residual standard deviation from the model

${\displaystyle {\textrm {response}}={\textrm {constant}}+0.5X_{i}}$

where X_i is the estimate of the i^th factor or interaction effect.

• The cumulative residual standard deviation that results from the model using the current term plus all terms preceding that term. That is,

${\displaystyle {\textrm {response}}={\textrm {constant}}+0.5\mathrm {(all\ effect\ estimates\ down\ to\ and\ including\ the\ effect\ of\ interest)} }$

This consists of a monotonically decreasing set of residual standard deviations (indicating a better fit as the number of terms in the model increases). The first cumulative residual standard deviation is for the model

${\displaystyle {\textrm {response}}={\textrm {constant}}}$

where the constant is the overall mean of the response variable. The last cumulative residual standard deviation is for the model

${\displaystyle {\textrm {response}}={\textrm {constant}}+0.5\mathrm {(all\ factor\ and\ interaction\ estimates)} }$

This last model will have a residual standard deviation of zero.

In most cases of least squares fitting, the model coefficients for previously added terms change depending on what was successively added. For example, the X₁ coefficient might change depending on whether or not an X₂ term was included in the model. This is not the case when the design is orthogonal, as is a 2³ full factorial design. For orthogonal designs, the estimates for the previously included terms do not change as additional terms are added. This means the ranked list of effect estimates simultaneously serves as the least squares coefficient estimates for progressively more complicated models.

From the above Yates output, one can define the potential models from the Yates analysis. An important component of a Yates analysis is selecting the best model from the available potential models. The above step lists all the potential models. From this list, we want to select the most appropriate model. This requires balancing the following two goals.

1. We want the model to include all important factors.
2. We want the model to be parsimonious. That is, the model should be as simple as possible.

In short, we want our model to include all the important factors and interactions and to omit the unimportant factors and interactions. Note that the residual standard deviation alone is insufficient for determining the most appropriate model as it will always be decreased by adding additional factors. Instead, seven criteria are utilized to define important factors. These seven criteria are not all equally important, nor will they yield identical subsets, in which case a consensus subset or a weighted consensus subset must be extracted. In practice, some of these criteria may not apply in all situations, and some analysts may have additional criteria. These criteria are given as useful guidelines. Mosts analysts will focus on those criteria that they find most useful.

1. Practical significance of effects
2. Order of magnitude of effects
3. Statistical significance of effects
4. Probability plots of effects
5. Youden plot of averages
6. Practical significance of residual standard deviation
7. Statistical significance of residual standard deviation

The first four criteria focus on effect sizes with three numeric criteria and one graphical criterion. The fifth criterion focuses on averages. The last two criteria focus on the residual standard deviation of the model. Once a tentative model has been selected, the error term should follow the assumptions for a univariate measurement process. That is, the model should be validated by analyzing the residuals.

Some analysts may prefer a more graphical presentation of the Yates results. In particular, the following plots may be useful:

1. Ordered data plot
2. Ordered absolute effects plot
3. Cumulative residual standard deviation plot

• Multi-factor analysis of variance
• Dex mean plot
• Block plot
• Dex contour plot

Box, G. E. P.; Hunter, W. G.; Hunter, J. S. (1978). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley and Sons. ISBN 0-471-09315-7.

 This article incorporates public domain material from the National Institute of Standards and Technology website https://www.nist.gov.

• Yates Analysis