Search results
Results from the WOW.Com Content Network
Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".
In statistics, econometrics and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set.
Small multiple map series showing the trends in partisan voting margins in Utah, 1900–2012. Small multiples are a popular technique in cartographic design for multivariate mapping . As with the small multiple chart, each panel uses the same underlying two-dimensional space, but in this case that is a geographic space.
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 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 small size and simplicity of the example allow simple validation of the rules of interpretation. But the method will be more valuable when the data set is large and complex. Other methods suitable for this type of data are available. Procrustes analysis is compared to the MFA in. [2]
The dinosaur data set created by Alberto Cairo that inspired the creation of the Datasaurus Dozen. The first data set, in the shape of a Tyrannosaurus, that inspired the rest of the "datasaurus" data set was constructed in 2016 by Alberto Cairo. [7] [8] It was proposed by Maarten Lambrechts that this data set also be called "Anscombosaurus". [7]
By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]