Introduction to Block Plot
A block plot is similar to a box plot, showing the importance of a particular factor in the data. [As shown in Fig 1] Just like ANOVA, it is used to show statistical significance.
Structure of Block Plot:
The structure of block plot includes:
- X-axis — All combinations of secondary variables/features from m to r.
- Y-axis — Response variable y. (significance measure)
We can use it when: data is of an extremely non-normal distribution. (Unlike ANOVA, where the data had to be of a normal distribution)
Interpreting a Block Plot
In a block plot, we deal with levels. (the numbers 1 and 2 are levels as shown in Fig 2) We can notice the following observations:
- If the levels of a block plot are uniform (i.e. all from 1->2 or 2->1), then that factor is significant to the data. [Block plot for Factor 1]
- If the levels of a block plot are non-uniform. (i.e some from 1->2 while some from 2->1), the factor under observation is not that significant. [Block plot for Factor 2]
The height of the bar determines the impact of that factor on the response variable.
- The block plot for FACTOR 1 is consistently taller, hence it is of higher significance. (i.e. FACTOR 1 is important)
- The block plot for FACTOR 2 is comparatively lower, hence making it less significant. (i.e. FACTOR 2 is not that important)
Advantages of Block Plot:
- The first advantage is that we can replace ANOVA (Analysis of Variance), a quantitative procedure with Block plot. (A graphical procedure)
- The second advantage is that a block plot has a lot of lesser assumptions to meet, unlike the other statistical tests. (for eg. it works well even for non-normal distribution of data)
A block plot is a powerful graphical technique that focuses on whether the factor is significant or not as well as answers a variety of important questions like how much the process has improved and shows a comparative analysis of the impact of various factors on that process. For any doubt/query, comment below.