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According to Denis Conniffe: Ronald A. Fisher was "interested in application and in the popularization of statistical methods and his early book Statistical Methods for Research Workers, published in 1925, went through many editions and motivated and influenced the practical use of statistics in many fields of study.
R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland. [10] The language was inspired by the S programming language, with most S programs able to run unaltered in R. [6] The language was also inspired by Scheme's lexical scoping, allowing for local ...
Kruschke's popular textbook, Doing Bayesian Data Analysis, [2] was notable for its accessibility and unique scaffolding of concepts. The first half of the book used the simplest type of data (i.e., dichotomous values) for presenting all the fundamental concepts of Bayesian analysis, including generalized Bayesian power analysis and sample-size planning.
Statistics educators have cognitive and noncognitive goals for students. For example, former American Statistical Association (ASA) President Katherine Wallman defined statistical literacy as including the cognitive abilities of understanding and critically evaluating statistical results as well as appreciating the contributions statistical thinking can make.
R (an open-source software environment for statistical computing, which includes several CART implementations such as rpart, party and randomForest packages), scikit-learn (a free and open-source machine learning library for the Python programming language). Weka (a free and open-source data-mining suite, contains many decision tree algorithms),
Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.
Overview of the Probably Approximately Correct (PAC) Learning Framework. An introduction to the topic. L. Valiant. Probably Approximately Correct. Basic Books, 2013. In which Valiant argues that PAC learning describes how organisms evolve and learn. Littlestone, N.; Warmuth, M. K. (June 10, 1986). "Relating Data Compression and Learnability" (PDF).
S is one of several statistical computing languages that were designed at Bell Laboratories, and first took form between 1975–1976. Up to that time, much of the statistical computing was done by directly calling Fortran subroutines; however, S was designed to offer an alternate and more interactive approach, motivated in part by exploratory data analysis advocated by John Tukey. [5]