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:

    This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.

    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:

    This course covers the statistical measurement and analysis methods relevant to the study of pharmacokinetics (the absorption, distribution and secretion of drugs), dose-response modeling, and bioequivalence. It provides practical work with actual/simulated clinical trial data.

    Aim of Course:


    This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.

    Aim of Course:

    In this course, participants will learn how to use the ggplot2 R Project to make, format, label and adjust graphs using R.

    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:

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

    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 covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.

    Aim of Course:


    This course covers the issues that need to be addressed in order for a study to produce statistically valid conclusions. The first session covers different study designs, with an emphasis on designs that can be used with observational data (clinical trials are reviewed briefly, but those seeking a more in depth treatment should take the Institute's sequence of courses in clinical trial design and analysis). Students will learn what types of bias and extraneous factors can endanger studies, and how to avoid or adjust for them.

    Aim of Course:

    This course covers key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.

    Aim of Course:

    This course will introduce you to the basic ideas of Bayesian Statistics. You will learn how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

    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:

    Continues the exploration of Rasch theory and its application in the Winsteps software, begun in "Practical Rasch Measurement-Core Topics." The course introduces exciting new topics and delves into earlier topics more deeply. Participants are encouraged to analyze their own datasets in parallel to the course datasets.

    Aim of Course:

    This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization 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.

    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:

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