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A simple case is where one has two data sets of the same size. In that case, to make the Q–Q plot, one orders each set in increasing order, then pairs off and plots the corresponding values. A more complicated construction is the case where two data sets of different sizes are being compared.
Chart Bar chart Box plot Correlogram Histogram Line chart Scatterplot Violin plot; ADaMSoft: Yes Yes Yes Yes Yes Yes Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes Yes Yes Yes Yes BMDP: Yes Yes ELKI: No No No Yes Yes Yes Epi Info: Yes No No Yes Yes Yes EViews: Yes Yes Yes Yes Yes Yes GAUSS: Yes Yes Yes Yes Yes GenStat: Yes Yes Yes Yes Yes Yes ...
As the above example illustrates, if two distributions are separated in space, the P–P plot will give very little data – it is only useful for comparing probability distributions that have nearby or equal location. Notably, it will pass through the point (1/2, 1/2) if and only if the two distributions have the same median.
It is possible to quickly compare several sets of observations by comparing their five-number summaries, which can be represented graphically using a boxplot. In addition to the points themselves, many L-estimators can be computed from the five-number summary, including interquartile range, midhinge, range, mid-range, and trimean.
In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space. The procedure thus appears to be the counterpart of principal component analysis for ...
make large data sets coherent; encourage the eye to compare different pieces of data; reveal the data at several levels of detail, from a broad overview to the fine structure; serve a reasonably clear purpose: description, exploration, tabulation, or decoration; be closely integrated with the statistical and verbal descriptions of a data set.
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
There are two important aspects of a Gage R&R: Repeatability: The variation in measurements taken by a single person or instrument on the same or replicate item and under the same conditions. [1] Reproducibility: the variation induced when different operators, instruments, or laboratories measure the same or replicated specimen. [1]