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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.

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Feature Overview
Data Analysis Applications
Video Demos
Tutorial Videos
Live Math Apps
Statistics Visualizations

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Advanced Features for Statistical Analytics:

Symbolic Statistics - input and output with mathematical notation.
Support for exact and arbitrary precision computations.
Define custom statistical models or use any of 38 predefined probability distributions.
Quickly generate large samples according to these distributions.
Apply standard statistical functions to data or statistical distributions.
Regression and analysis of variance.
Curve fitting and interpolation of regular and irregularly spaced data.
Dimensionality reduction techniques.
Test hypotheses according to 11 different automated hypothesis testing routines.
Statistical process control.
Specialized tools and features for teaching introductory probability and statistics courses.
Collection of interactive statistical “MathApp” documents in Maple as well as in the MapleCloud.
Extensive library of mathematical packages, including linear algebra, optimization, and more.
And much more! Learn more in the Statistics Package Overview.

Data Analysis & Applications

Working with Data Sets in Maple

Maple's Data Sets package provides easy access to data from various built-in and online data sources. In this video, we will walk through examples of working with several types of data in Maple, including visualizing stock and commodity data, forecasting future temperatures using weather data, and analyzing macroeconomic data, such as employment statistics, GDP and other economic indicators.

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View more Data Analysis and Applications Videos

Organize and query heterogeneous data with labelled DataSeries and DataFrames.
Create dynamic 2-D or 3-D visualizations or animations.
Compute cross-correlation and autocorrelation of data samples or time series data.
Data smoothing and filtering commands.
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.
Run Python code using a built-in Python kernel.
Deep Learning algorithms with TensorFlow.

More Features and Examples

Distributions and Random Variables

Hypothesis Testing and Inference

Robust Statistics

Statistical Estimation

Data Smoothing

Time Series: Forecasting Air Passenger Data

Time Series: Forecasting Average Global Temperature

Principal Component Analysis on the Iris data set

Statistics with DataFrames

Subsets of DataFrames

Statistics Package Features Videos

Introduction to the Statistics Package
This introductory webinar will demonstrate Maple's statistics and data analysis capabilities, including a general overview of the Statistics Package.

Topics in Advanced Statistics: Cumulants, k-statistics,
and umbral calculus
This webinar will explore the definition of cumulants, as well as how to estimate them using k-statistics.

Statistics Tutorial Videos

Descriptive Statistics

Measures of Central Tendency
In this video, learn more about how to use clickable statistics tools to compute the mean, median and mode of a collection of data in Maple.

Measures of Spread
In this video, learn more about methods for finding the range, quartiles, and interquartile range of a collection of data.

Variance and Standard Deviation
In this video learn more about finding the variance and standard deviation of a collection of data.

Comparing Two Sets of Data
In this video, see how two collections of data can be compared with respect to measures of central tendency and measures of spread.

Standard Scores
In this video, see examples of how you can compare observations from two different distributions.

Creating Quizzes for Descriptive Statistics
This video provides examples on how to use Maple's tools for easy application creation to generate training material for students in statistics.

Inferential Statistics

Hypothesis Tests
In this video, you can learn more about the various hypothesis tests available in the Student Statistics package, as well as the interactive hypothesis 'Test Guide' tutor.

Z-Tests
The z-test is used to compare means of two distributions with known variance. In this video, see an example of applying the one sample z-test to a sample of data.

Random Variables and Probability Distributions

Random Variables
This video will discuss how well sample statistics estimate population parameters and show how taking larger samples can better estimate population parameters.

Sampling
In this video, you can learn more about the various hypothesis tests available in the Student Statistics package, as well as the interactive hypothesis 'Test Guide' tutor.

The Normal Distribution
This video will discuss how well sample statistics estimate population parameters and show how taking larger samples can better estimate population parameters.

Data Analysis and Applications Videos

A Guide to DataFrames
This is a guide to DataFrames in Maple. This video seeks to answer many commonly asked questions on DataFrames such as How do I sort a DataFrame, How do I filter data, and many, many more.

Interpolating Unstructured Data in Maple
Learn about new tools for interpolating unstructured, irregularly spaced data in Maple.

Importing Tabular Data
In this video, see some short examples of using Maple commands such as Import, ImportMatrix, ExcelTools:-Import, and scanf to read in data from files stored on disk.

Introduction to Regression Analysis
The primary focus of the video is the use of the Statistics:-Fit command to fit a model to data as well as generating reports on the fitted model.

The Dataplot Command
This webinar shows the features of Maple’s dataplot command, which is used to plot datasets in a variety of ways.

Least Squares Fitting in Maple
In this webinar, Dr. Lopez will illustrate the use of, and interaction between, these commands.

Principal Component Analysis:
Learn about Maple’s computation and visualization tools for principal component analysis, through an exploration of Fisher’s Iris flowers data set.

An Introduction to Data Frames:
In this video, learn more about how to create a DataFrame and DataSeries in Maple.

Aggregate Statistics on DataFrames
This video will walk through a short example of how the Aggregate command can be used to compute summary statistics on columns of a DataFrame.

Code Generation for R
This video shows examples of translating Maple code, including expressions, procedures, data structures, and commands from Maple’s Statistics package to equivalent code in the R language.

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.

Binomial Distribution

Box Plots

Exponential Distribution

Histogram

Measures of Spread

Poisson Distribution

Stem and Leaf Displays

Z-Scores

Z-Tests

Rolling Two Dice

Geometric Distribution

Chi-Square Distribution

Hypergeometric Distribution

Normal Approximation of Binomial Distribution

Software Solutions to Enhance Statistics Education

Download Whitepaper

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.

Searching, Exploring and Visualizing Data using Maple

Download Whitepaper

With the ever-expanding sea of available data and data repositories, navigating the vast quantities of information can be a difficult task. Maple, the advanced computing software from Maplesoft, is a powerful, interactive data visualization tool.

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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.

Working with Data Sets in Maple

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