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Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value. A boxplot is a standardized way of displaying the dataset based on the five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.
The box plot above, using the CLD methodology, is now far more informative. The cities are sorted in descending order from left to right. The color density is tiered with the cities having higher rainfall being colored with more dense or opaque tones; meanwhile, the cities with lower rainfall have less dense or more transparent tones.
Box plot of the Michelson–Morley experiment, showing several summary statistics. In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in
Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots.
More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot. In the business world, descriptive statistics provides a useful summary of many types of data.
Aickin's α; Andres and Marzo's delta; Bangdiwala's B; Bennett, Alpert, and Goldstein’s S; Brennan and Prediger’s κ; Coefficient of colligation - Yule's Y; Coefficient of consistency
The IQR is used to build box plots, simple graphical representations of a probability distribution. The IQR is used in businesses as a marker for their income rates. For a symmetric distribution (where the median equals the midhinge, the average of the first and third quartiles), half the IQR equals the median absolute deviation (MAD).
loglinear analysis (to identify relevant/important variables and possible confounders) Exact tests or bootstrapping (in case subgroups are small) Computation of new variables; Continuous variables Distribution Statistics (M, SD, variance, skewness, kurtosis) Stem-and-leaf displays; Box plots