1.
Exploratory Data Analysis
1.3. EDA Techniques 1.3.3. Graphical Techniques: Alphabetic


Purpose: Check for a change in location, variation, or distribution 
The bihistogram is an EDA tool for assessing whether a
beforeversusafter engineering modification has caused a change in


Sample Plot: This bihistogram reveals that there is a significant difference in ceramic breaking strength between batch 1 (above) and batch 2 (below) 
From the above bihistogram, we can see that batch 1 is centered at a ceramic strength value of approximately 725 while batch 2 is centered at a ceramic strength value of approximately 625. That indicates that these batches are displaced by about 100 strength units. Thus the batch factor has a significant effect on the location (typical value) for strength and hence batch is said to be "significant" or to "have an effect". We thus see graphically and convincingly what a ttest or analysis of variance would indicate quantitatively. With respect to variation, note that the spread (variation) of the aboveaxis batch 1 histogram does not appear to be that much different from the belowaxis batch 2 histogram. With respect to distributional shape, note that the batch 1 histogram is skewed left while the batch 2 histogram is more symmetric with even a hint of a slight skewness to the right. Thus the bihistogram reveals that there is a clear difference between the batches with respect to location and distribution, but not in regard to variation. Comparing batch 1 and batch 2, we also note that batch 1 is the "better batch" due to its 100unit higher average strength (around 725). 

Definition: Two adjoined histograms 
Bihistograms are formed by vertically juxtaposing two histograms:


Questions 
The bihistogram can provide answers to the following questions:


Importance: Checks 3 out of the 4 underlying assumptions of a measurement process 
The bihistogram is an important EDA tool for determining if a factor "has an effect". Since the bihistogram provides insight into the validity of three (location, variation, and distribution) out of the four (missing only randomness) underlying assumptions in a measurement process, it is an especially valuable tool. Because of the dual (above/below) nature of the plot, the bihistogram is restricted to assessing factors that have only two levels. However, this is very common in the beforeversusafter character of many scientific and engineering experiments.  
Related Techniques 
t test (for shift in location) F test (for shift in variation) KolmogorovSmirnov test (for shift in distribution) Quantilequantile plot (for shift in location and distribution) 

Case Study  The bihistogram is demonstrated in the ceramic strength data case study.  
Software  The bihistogram is not widely available in general purpose statistical software programs. Bihistograms can be generated using Dataplot and R software. 