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:

    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.

    Aim of Course:

    The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.

    Aim of Course:

    The purpose of this online course is to teach you how to extract data from a relational database using SQL, so that you can perform statistical operations.  The focus is on structuring queries to extract structured data (not on building databases or methods of handling big data).

    Aim of Course:

    In this course, you'll learn everything you need to get you started using Python for data analysis

    In this course, you'll learn everything you need to get you started using Python for data analysis. - See more at: http://www.statistics.com/course-catalog/python/#syllabus

    Aim of Course:

    This course covers the crafting of survey questions, the design of surveys, and different sampling procedures that are used in practice. Longstanding basic principles of survey design are covered, and the impact of the trend toward increased respondent resistance is discussed.

    Aim of Course:

    To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

    Aim of Course:

    This course will teach you how to design, monitor and analyze clinical trials using statistically sound principles that incorporate interim looks at the data, possible early stopping, and sample size re-estimation at interim.

    Aim of Course:

    Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

    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:

    In this online course you will learn you how to apply predictive modeling methods, and persuasion (uplift) models in particular.  The focus will be on targeting voters in political campaigns.

    Aim of Course:

    This course is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP).

    Aim of Course:

    This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.

    Aim of Course:

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

    Aim of Course:

    Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

    Aim of Course:

    This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.