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The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham [1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data. [2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3 ...
The following example modified from pbdMPI illustrates the basic syntax of the language of pbdR. Since pbdR is designed in SPMD, all the R scripts are stored in files and executed from the command line via mpiexec, mpirun, etc. Save the following code in a file called "demo.r"
Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law.
R is a programming language for statistical computing and data visualization.It has been adopted in the fields of data mining, bioinformatics and data analysis. [9]The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to ...
P. Westfall, R. Tobias, R. Wolfinger (2011) Multiple comparisons and multiple testing using SAS, 2nd edn, SAS Institute A gallery of examples of implausible correlations sourced by data dredging [1] An xkcd comic about the multiple comparisons problem, using jelly beans and acne as an example
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. [1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.
There are two approaches to statistical inference: model-based inference and design-based inference. [2] [3] [4] Both approaches rely on some statistical model to represent the data-generating process. In the model-based approach, the model is taken to be initially unknown, and one of the goals is to select an appropriate model for inference ...