Exploratory Data Analysis
1.3. EDA Techniques
1.3.5. Quantitative Techniques
Estimate Factor Effects in a 2-Level Factorial Design
Full factorial and
factorial designs are common in
for engineering and scientific applications.
In these designs, each factor is assigned two levels. These are typically called the low and high levels. For computational purposes, the factors are scaled so that the low level is assigned a value of -1 and the high level is assigned a value of +1. These are also commonly referred to as "-" and "+".
A full factorial design contains all possible combinations of low/high levels for all the factors. A fractional factorial design contains a carefully chosen subset of these combinations. The criterion for choosing the subsets is discussed in detail in the process improvement chapter.
The Yates algorithm exploits the special structure of these designs to generate least squares estimates for factor effects for all factors and all relevant interactions.
The effect estimates are typically complemented by a number of graphical techniques such as the DOE mean plot and the DOE contour plot ("DOE" represents "design of experiments"). These are demonstrated in the eddy current case study.
Before performing the Yates algorithm, the data should be
arranged in "Yates order". That is, given k factors, the
kth column consists of 2k-1 minus
signs (i.e., the low level of the factor) followed by
2k-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
- - - + - - - + - + + - - - + + - + - + + + + +
Determining the Yates order for fractional factorial designs requires knowledge of the confounding structure of the fractional factorial design.
The Yates algorithm is demonstrated for the
data set. The data set contains eight measurements from a
two-level, full factorial design with three factors. The
purpose of the experiment is to identify factors that
have the most effect on eddy current measurements.
In the "Effect" column, we list the main effects and interactions from our factorial experiment in standard order. In the "Response" column, we list the measurement results from our experiment in Yates order.
Effect Response Col 1 Col 2 Col 3 Estimate ------ -------- ----- ----- ----- -------- Mean 1.70 6.27 10.21 21.27 2.65875 X1 4.57 3.94 11.06 12.41 1.55125 X2 0.55 6.10 5.71 -3.47 -0.43375 X1*X2 3.39 4.96 6.70 0.51 0.06375 X3 1.51 2.87 -2.33 0.85 0.10625 X1*X3 4.59 2.84 -1.14 0.99 0.12375 X2*X3 0.67 3.08 -0.03 1.19 0.14875 X1*X2*X3 4.29 3.62 0.54 0.57 0.07125 Sum of responses: 21.27 Sum-of-squared responses: 77.7707 Sum-of-squared Col 3: 622.1656The first four values in Col 1 are obtained by adding adjacent pairs of responses, for example 4.57 + 1.70 = 6.27, and 3.39 + 0.55 = 3.94. The second four values in Col 1 are obtained by subtracting the same adjacent pairs of responses, for example, 4.57 - 1.70 = 2.87, and 3.39 - 0.55 = 2.84. The values in Col 2 are calculated in the same way, except that we are adding and subtracting adjacent values from Col 1. Col 3 is computed using adjacent values from Col 2. Finally, we obtain the "Estimate" column by dividing the values in Col 3 by the total number of responses, 8.
We can check our calculations by making sure that the first value in Col 3 (21.27) is the sum of all the responses. In addition, the sum-of-squared responses (77.7707) should equal the sum-of-squared Col 3 values divided by 8 (622.1656/8 = 77.7707).
The Yates algorithm provides a convenient method for computing effect estimates; however, the same information is easily obtained from statistical software using either an analysis of variance or regression procedure. The methods for analyzing data from a designed experiment are discussed more fully in the chapter on Process Improvement.
The following plots may be useful to complement the
quantitative information from the Yates algorithm.
The Yates algorithm can be used to answer the following
Multi-factor analysis of variance
DOE mean plot
DOE contour plot
|Case Study||The analysis of a full factorial design is demonstrated in the eddy current case study.|
|Software||All statistical software packages are capable of estimating effects using an analysis of variance or least squares regression procedure.|