Meta analysis, the ‘analysis of analyses’, is the term used to describe
the quantitative synthesis of scientific evidence. The aim of this
course is to introduce students to the fundamentals of meta-analysis and
provide an in-depth review of tools for conducting meta-analyses in the
R language. The course will cover the fundamentals of the fixed and
random effects models for meta-analysis, the assessment of
heterogeneity, and evaluating bias.
In this online course, you will learn how to examine data with the goal of detecting anomalies or abnormal instances. This task is critical in a wide range of applications ranging from fraud detection to surveillance.
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.
This course teaches you how to estimate variances for complex surveys, and also how to model the results using linear and logistic regression, and other generalized linear models.
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.
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.
Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.
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).
To provide an easy introduction to statistical inference for a single variable.
Aim of Course:
After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. This is a course to "Learn R via your existing knowledge of basic statistics" and does not treat statistical concepts in depth. Be sure to read the prerequisites.
Try out our introductory statistics series for one week - you can learn stats!
The aim of the course is to give you the skills to work with a variety of data types and data sources in R.
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.
This course covers a number of advanced topics in optimization. Students taking this course will learn to specify and implement optimization models that solve network problems (what is the shortest path through a network, what is the least cost way to route material through a network with multiple supply nodes and multiple demand nodes).
This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.
This course covers the analysis of data gathered in surveys.
After taking this course, you will be equipped to introduce natural language processing (NLP) processes into your projects and software applications.
This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
In this online course, “R Programming Intro 1,” you will be introduced
to basic concepts in computer programming via R - it is for those who
have had little or no experience in programming.