R
The statistical language R has proven to be a popular choice among statisticians, researchers, and organizations for statistical analysis and graphs. This is partly due to the free nature of the program and the power of its programming capabilities. Other strengths include its exceptionally good support, its continual improvement (updates occur almost monthly at times), and its very active user community. R is a model example of the benefits of free and open-source software, and a wealth of contributed documentation is freely available. In recent years, the R language has amassed a growing, and very strong, supporting literature, which consists of several introductory texts and companions to existing textbooks and which implements modern statistical methods, regression modeling strategies, and specialized types of models. R integrates ...
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Reader's Guide
Descriptive Statistics
Distributions
Graphical Displays of Data
Hypothesis Testing
Important Publications
Inferential Statistics
Item Response Theory
Mathematical Concepts
Measurement Concepts
Organizations
Publishing
Qualitative Research
Reliability of Scores
Research Design Concepts
Research Designs
Research Ethics
Research Process
Research Validity Issues
Sampling
Scaling
Software Applications
Statistical Assumptions
Statistical Concepts
Statistical Procedures
Statistical Tests
Theories, Laws, and Principles
Types of Variables
Validity of Scores
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