M215: Applied Statistical Analysis with Mathematica

Course Objective This short course series is designed to give experience with the statistical tools that are available in Mathematica. Using real-world and simulated data sets, participants will import data, extract parts of the data based on various criteria sets, analyze the data, and visualize the results.

Presenter The course is presented by a Wolfram Research senior developer or a Wolfram Education Group certified instructor.

Target Audience The course is designed for people who work with data and wish to improve their skills at using Mathematica for performing statistical analyses of data. Typical attendees include engineers, physicists, analysts in finance, and those in the physical sciences and the life and medical sciences.

Delivery Type This short course consists of two sections: M215A and M215B. Individual sections are each three to four hours long and offered online. Attendees may take one or both of these sections in whichever order they wish. The two sections can be combined into a full-day course, offered in a computer classroom facility. Additional online training information is available.

Syllabus

M215A: Applied Statistical Analysis with Mathematica: Descriptive and Mathematical Statistics

Computing basic descriptive statistics of data (mean, median, variance, etc.)

Visualizing statistical data (including box plots, scatter plots, and histograms)

Computing and visualizing properties of continuous and discrete distributions, such as mean, PDF, CDF, expectations, and quantiles

Random number generation from continuous and discrete distributions

Hypothesis testing (including t-tests, z-tests, and chi-squared tests) and confidence intervals

Analysis of variance (ANOVA)

M215B: Applied Statistical Analysis with Mathematica: Regression

Curve fitting via ordinary least squares, alternative metrics, and merit functions

Linear and nonlinear regression

Obtaining and visualizing regression diagnostics

Data transformations

Robust regression via iterative reweighting

Maximum likelihood estimation

Fitting generalized linear models

Course Materials Each attendee will be provided with Mathematica course notebooks and access to the current version of Mathematica. The course notebooks require Mathematica or Mathematica Player. For attendees participating in classroom-based sessions, course materials are distributed in print and on CD-ROM, and are yours to keep; a computer running Mathematica is available for your use during class. For attendees participating in online classes, a download of the course materials is provided; a temporary Mathematica training license is provided upon request.

Prerequisites Course attendees are expected to have basic familiarity with Mathematica approximately equivalent to that provided by "M101: A First Course in Mathematica." Attendees should also have basic knowledge of descriptive statistics, mathematical statistics (to a lesser extent), hypothesis testing, ANOVA, and regression.