Statistics and Data Analysis - Maple Features - Maplesoft


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Statistics and Data Analysis

Combining a comprehensive set of algorithms, powerful numerical and symbolic capabilities, and a rich, intuitive authoring environment, Maple is the ideal tool for your predictive modeling and simulation projects.

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Advanced Features for Statistical Analytics:
  • Use any of the 38 predefined distributions, or define your own; put in symbolic parameters; and compute as many as 45 different properties of the resulting random variable, such as the expected value, the kurtosis, or the cumulant generating function.
  • Compute the same properties for arbitrary algebraic expressions involving random variables.
  • Quickly generate enormous samples of each of these distributions, take advantage of many specialized visualization routines, and compute cross-correlation and autocorrelation of data samples.
  • Determine the maximum-likelihood estimate of one or more distribution parameters from a sample that is either given concretely or that consists of symbolic values.
  • Run data smoothing to extract identifiable patterns from noisy data.
  • Use interactive assistants and templates to easily access the tremendous power of this package and get results quickly.
  • Test hypotheses according to 11 different automated hypothesis testing routines.
  • And much more! Learn more in the Statistics Package Overview.
Data Analysis & Applications
More Features and Examples
Statistics Package Features Videos
Statistics Tutorial Videos
Descriptive Statistics
Inferential Statistics
Random Variables and Probability Distributions
Data Analysis and Applications Videos
Live Math Apps from the MapleCloud

Maple Math Apps provide demonstrations that illustrate various mathematical and scientific concepts. The following are live examples that can be interacted with online, or downloaded for use in Maple or the free Maple Player.

Software Solutions to Enhance Statistics Education

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For many years, technology has proved its usefulness in the mathematics classroom. Advances in symbolic computation and user interface design have resulted in tools that make it easy for instructors and students to explore concepts, experiment with ‘what if’ scenarios, visualize results, and solve engaging real-world problems. Statistics classrooms have also made use of technology, but unfortunately many of the traditional tools are harder to use, less advanced, or less flexible than those available for mathematics, making it more difficult for both students and instructors to achieve the same benefits.

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