The Institute for Statistics Education at             Statistics.com

is the leading provider of online education in statistics.
We offer 100+ courses in introductory and advanced statistical subjects.
Students from around the world study with leading authorities via private discussion boards
on flexible schedules.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.

    Available courses

    Aim of Course:

    The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

    Aim of Course:

    This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models. It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. The course aims at providing a basic understanding and knowledge of the mixed effect models that will allow you to use them in practice.

    Aim of Course:

    This course covers the application of Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

    Aim of Course:

    You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning Tools.” You will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The full course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

    Aim of Course:

    Try out our introductory statistics series for one week - you can learn stats!

    Aim of Course:

    This course will offer an introduction to sample size and power analysis and will show how to use it simply and effectively to plan the appropriate sample size for a study.

    Aim of Course:

    This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.

    Aim of Course:

    This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.

    Aim of Course:

    This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.

    Aim of Course:

    This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

    Aim of Course:

    This course will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories).

    Aim of Course:

    This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

    Aim of Course:

    The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

    Aim of Course:

    This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.

    Aim of Course:

    To provide an easy introduction to statistical inference for a single variable.

    Aim of Course:

    After taking this course, you will be able to install and run rjags, a program for Bayesian analysis within R.  Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.

    Aim of Course:

    This course covers the basic statistical principles in the design and analysis of randomized controlled trials.

    Aim of Course:

    This course will teach you how to choose an appropriate time series
    forecasting method, fit the model, evaluate its performance, and
    use it for forecasting.

    Aim of Course:

    Try out our introductory statistics series for one week - you can learn stats!

    Aim of Course:

    This course will stress the application of DOE rather than statistical theory. With a 12-step checklist, it covers full and fractional factorial designs, Plackett-Burman, Box-Behnken, Box-Wilson and Taguchi designs.