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Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder–Richardson Formula 20; Linear ...
Soft independent modelling by class analogy (SIMCA) is a statistical method for supervised classification of data. The method requires a training data set consisting of samples (or objects) with a set of attributes and their class membership. The term soft refers to the fact the classifier can identify samples as belonging to multiple classes ...
tidyr – help transform data specifically into tidy data, where each variable is a column, each observation is a row; each row is an observation, and each value is a cell. readr – help read in common delimited, text files with data; purrr – a functional programming toolkit; tibble – a modern implementation of the built-in data frame data ...
This list of museums in Ohio is a list of museums, defined for this context as institutions (including nonprofit organizations, government entities, and private businesses) that collect and care for objects of cultural, artistic, scientific, or historical interest and make their collections or related exhibits available for public viewing.
Data wrangling can benefit data mining by removing data that does not benefit the overall set, or is not formatted properly, which will yield better results for the overall data mining process. An example of data mining that is closely related to data wrangling is ignoring data from a set that is not connected to the goal: say there is a data ...
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 categorical data. [citation needed] MCA can be viewed as an extension of simple correspondence analysis (CA) in that it is applicable to a large set of categorical variables.
New Mexico History Museum: New York: New-York Historical Society: New York State Museum: North Carolina: North Carolina State Historic Preservation Office: North Carolina Museum of History: North Dakota: State Historical Society of North Dakota: North Dakota Heritage Center: Ohio: Ohio History Connection: Ohio History Center: Oklahoma: Oklahoma ...
It is called a latent class model because the class to which each data point belongs is unobserved, or latent. Latent class analysis (LCA) is a subset of structural equation modeling, used to find groups or subtypes of cases in multivariate categorical data. These subtypes are called "latent classes". [1] [2]