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There are distribution-free control charts for both Phase-I analysis and Phase-II monitoring. One of the most notable distribution-free control charts for Phase-I analysis is RS/P chart proposed by G. Capizzi and G. Masaratto. RS/P charts separately monitor location and scale parameters of a univariate process using two separate charts.
Those who promote Bayesian inference view "frequentist statistics" as an approach to statistical inference that is based on the frequency interpretation of probability, usually relying on the law of large numbers and characterized by what is called 'Null Hypothesis Significance Testing' (NHST). Also the word "objective", as applied to ...
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as in parametric statistics. [1] Nonparametric statistics can be used for descriptive statistics or statistical ...
Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form. They include plots such as scatter plots , histograms , probability plots , spaghetti plots , residual plots, box plots , block plots and biplots .
The range-bar method was first introduced by Mary Eleanor Spear in her book "Charting Statistics" in 1952 [4] and again in her book "Practical Charting Techniques" in 1969. [5] The box-and-whisker plot was first introduced in 1970 by John Tukey , who later published on the subject in his book "Exploratory Data Analysis" in 1977.
The above eight rules apply to a chart of a variable value. A second chart, the moving range chart, can also be used but only with rules 1, 2, 3 and 4. Such a chart plots a graph of the maximum value - minimum value of N adjacent points against the time sample of the range.
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.
The usage of thin slices, which are hard to discern, may be difficult to interpret. [7] The usage of percentages as labels on a pie chart can be misleading when the sample size is small. [8] Making a pie chart 3D or adding a slant will make interpretation difficult due to distorted effect of perspective. [9]