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 interim re-estimation of power and required sample size. It covers group sequential designs and adaptive methods of sample-size re-estimation.

The aim of the course is to present advanced and important concepts that have received very little attention, such as designs for irregular experimental regions and Analysis of Means (ANOM).

This course presents cumulative sum (CUSUM) procedures and exponentially weighted average (EWMA) methods and their variations for monitoring processes.

This course covers many popular advanced SEM models with practical exercises. Models covered include Multiple Indicator an Multiple Causes models (MIMIC), Multiple Group models, Multilevel (HLM) models, Mixture models, Structured Means models, Multitrait-Multimethod models, Second Order Factor models, Interaction models, and Dynamic Factor models.

This is a second level epidemiology course that emphasizes methods for analyzing epidemiologic data. Topics covered in the course include: simple analysis of 2x2 tables, control of extraneous variables (including an introduction to logistic regression), stratified analysis, and matching.

This course will review various types of nonnormal data, and survey the statistical techniques require to analyze them.

This course covers the essentials of Bayesian analysis in randomized clinical trials and the evaluation of medical interventions. We will discuss the distinctions between Bayesian analysis and the more traditional frequentist analysis, and the pros and cons of each approach.

This online course covers the application of Bayesian statistical methods to environmental data and decision-making.

This is a second level epidemiology course that emphasizes the underlying concepts and methods for addressing validity and bias issues in epidemiologic research. Topics covered in the course include: overview of validity and bias, selection bias, information bias, and confounding bias.

This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.

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). Topics include tests for independence (comparing proportions as well as chi-square), exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.

This course continues the analysis of categorical data, contains a review of logistic regression, and introduces multinomial responses for logistic regression, probit, logit and loglinear analysis.

This course covers the essential concepts required to design rigorous randomized trials so as to ensure valid treatment comparisons.

This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.

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.

Rule induction is an important component of data mining, and this course covers two main styles of generating rules.

To design a product, respond to competitors or anticipate their moves, or develop pricing strategies, decision-makers need to integrate answers to such questions in a quantitatively useful fashion. Conjoint analysis is a marketing research technique that asks respondents to rank, rate, or choose among multiple products or services, where each product is described using multiple characteristics. The researcher uses experimental designs to manipulate the appearance of attribute levels in product concepts. After the data are collected, the researcher uses statistical methods to infer how the product attribute levels drive preference or choice. The researcher can use the resulting model to model how the market would choose among a set of competing product alternatives.

This course covers sampling methods and analyses used to study of the density and abundance of animals and plants, and other important biological variables.

The topics covered in this course include prediction intervals, tolerance intervals, calibration intervals, measurement error, accelerated life testing, measurement system appraisal, reliability and lifetime testing.

This course teaches participants how to model financial events that have uncertainties associated with them.

This course will teach you how to choose an appropriate time series model, fit the model, to conduct diagnostics, and use the model for forecasting. The course will focus on Autoregressive (AR), Moving Average (MA), combined ARMA, and Box Jenkins type models.

This course covers multiple linear regression, autoregressive modesls (ARMA and ARIMA), seasonal adjustment, measures of forecast accuracy, and exponential smoothing. It goes into greater detail and depth that "Forecasting Time Series 1."

This is an introductory epidemiology course that emphasizes the underlying concepts and methods of epidemiology. Topics covered in the course include: study designs (clinical trials, cohort studies, case-control studies, and cross-sectional), measures of disease frequency and effect.

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. GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Logistic, Poisson, and negative binomial regression models are three of the most noteworthy GLM family members.

The aim of this course is to teach you how to produce statistical plots of data using the R language and environment for statistical computing and graphics. The creation of standard plots such as scatterplots, bar plots, histograms, and boxplots will be covered and time will be spent on the underlying model used to produce plots in R so that you can extensively customize these plots.

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.

This course covers the principal statistical concepts used in medical and health sciences. Basic concepts common to all statistical analysis are reviewed, and those concepts with specific importance in biostatistical are covered in detail.

This course covers the two core paradigms that account for most business applications of data mining: classification and prediction. In both cases, data mining takes data where a variable of interest is known and develops a model that relates this variable to a series of predictor variables. In classification, the variable of interest is categorical ("purchased something" vs. "has not purchased anything"). In prediction, the variable of interest is continuous ("dollars spent"). Four techniques will be used: k-nearest neighbors, classification and regression trees (CART), logistic regression and multiple linear regression. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model).

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 Teguchi designs.

This course will cover the most important principles, techniques and tools used in modeling in Quantitative Risk Analysis.

This course will provide an easy introduction to R and its use in organizing and exploring data. Once you've completed this course you'll be able to enter, save, retrieve, manipulate, summarize and display data using R.

See also the related course "Introduction to R - Statistical Analysis," which is an introduction to the use of R for executing statistical tests and procedures.

This course covers how to use R for basic statistical procedures.

The course introduces the basic concepts and methods of resampling methods, including bootstrap procedures and permutation (randomization) tests, with little or no complex theory or confusing notation.

To provide an easy introduction to statistical inference for a single variable. Once you've completed this course you'll be able to apply statistically valid designs to basic studies, and test hypotheses regarding proportions and means.

To provide an easy introduction to statistical inference for a single variable. Once you have completed this course you will be able to apply statistically valid designs to basic studies, and test hypotheses regarding proportions and means.

The aim of this course is to provide an easy introduction to inference for two variables through a series of practical applications. Once you have completed this course you will be able to test hypotheses regarding a simple regression or a comparison of proportions or two means.

The aim of this course is to provide an easy introduction to inference for two variables through a series of practical applications. Once you have completed this course you will be able to test hypotheses regarding a simple regression or a comparison of proportions or two means.

This course provides an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

To provide an easy introduction to statistics for those with little or no prior exposure to basic probability and descriptive statistics.

This course covers the fundamental concepts and theory of Structural Equation Modeling -- describing the relationships between variables. Case studies are used and AMOS software is introduced.

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.

This course will cover the analysis and interpretation of judge-intermediated ratings, like essay grading, Olympic ice-skating, therapist ratings of patient behavior, etc.

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

This course will cover the theory and practice of two modern methods of handling missing data: maximum likelihood and multiple imputation.

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.

This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model.

This course covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data.

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

This online course at statistics.com covers the essentials of National Income accounting.

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

This course teaches you how to perform a range of nonparametric statistical analyses - robust procedures that do not assume that data are samples from prespecified families of distribution such as the normal or exponential.

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.

Continues the exploration of Rasch theory and its application in the Winsteps software, begun in "Practical Rasch Measurement-Core Topics." The visual step-by-step tutorial propels you along the road to success. The Course introduces exciting new topics and delves into earlier topics more deeply.

Online course in randomization (permutation) tests.

In this course you will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

This course shows you how to make power and sample size determination for experiments, surveys and long-term trials.

Spatial statistical analysis uses methods adapted from conventional statistics to address problems in which spatial location is the most important explanatory variable. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.

This course will acquaint you with the process of microarray data mining from beginning to end. You will learn how to how to preprocess the data, estimate gene expression patterns, cluster genes to detect interesting gene expression patterns, and classify experiments (subjects) based on gene expression patterns. Illustrations of the statistical issues involved at the various stages of the analysis will use real data sets from DNA microarray experiments.

This course will will go beyond the basics of SPC and present some improved control chart methods, with better ways of determining control limits.

This course will introduce you to the statistical methods used in environmental analysis. Many of these methods would be covered in a standard course on statistics, but some of the topics that are covered here would not be included in such a course.

This course covers the analysis of data gathered in surveys.

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.

This course provides an easy introduction to statistics and statistical terminology through a series of practical applications. Once you've completed this course you'll be able to summarize data and interpret reports and newspaper accounts that use statistics and probability. You'll use simulation and resampling to fully grasp the difficult concept of "statistical significance."

The course describes the various methods used for modeling and evaluating survival data, or time-to event data.

This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text.

The aim of this course is to walk through the U.S. Census Bureau's new American Community Survey. This Survey is the cornerstone of the new decennial census methodology and will replace the long form in future censuses. Currently, analysts, managers and planners must make do with increasingly out-of-date detailed information about the characteristics of the population and housing while awaiting the next decennial census.