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:

    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:

    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.

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    Meta-Analysis refers to the statistical analyses that are used to synthesize summary data from a series of studies. This course covers some advanced issues in meta-analysis. Participants should have completed the basic course in meta-analysis or an equivalent.

    Aim of Course:

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

    Aim of Course:


    The course introduces the use of mathematical models for managerial decision making and covers how to formulate linear programming models for decision problems where multiple decisions need to be made in the best possible way while simultaneously satisfying a number of logical conditions (or constraints). You will learn how to use spreadsheet software to implement and solve these linear programming problems.

    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:

    Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.

    Aim of Course:

    This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

    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:

    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:

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

    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 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:

    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.

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    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:

    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 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 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 will show you how to use R to create statistical models and use them to analyze data.

    Aim of Course:

    In this online course, "Programming 2:Python," you'll learn everything you need to get you started using Python for data analysis.  We'll review basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products.