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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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.

**Learn More:**

- 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.

- Organize and query heterogeneous data with rich labelled DataFrames and DataSeries.
- Access over 12 million curated time series data sets with Maple’s built-in link to Quandl.
- Data import & export for many supported file types.
- Excel add-in to run Maple code inside of Excel and support for import and export to Excel file types.
- Internet Connectivity to connect to data feeds, online databases and other sources of information.
- Matlab Connectivity: Use Maple for symbolic computations inside of Matlab.
- Translate your Maple code to various target languages including R, Python, Matlab, C and more.
- Data visualization: Create dynamic data visualizations with Live Data Plots and the dataplot command.
- Tools for Curve fitting, regression analysis, Time Series Analysis, Finance and Process Control.
- Specialized tools and features for Statistics Education.

Descriptive Statistics

Inferential Statistics

Random Variables and Probability Distributions

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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