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.
- Teacher: Meena Badade
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
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.
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:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Peter Bruce
Aim of Course:
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. In marketing applications, clusters of customer records are called market segments (and the process is called market segmentation).
- Teacher: Anthony Babinec
Aim of Course:
This course covers the fundamental concepts and theory of Structural Equation Modeling: model specification, model identification, model estimation, model testing, and model modification.
- Teacher: Randall Schumacker
Aim of Course:
Structural Equation Modeling (SEM) is a modeling technique that allows
you to create a deeper understanding of how your data is structured. You
will learn how to create structural equation models using the lavaan package in R.
- Teacher: Erin Buchanan
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.
- Teacher: Everett Smith
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
- Teacher: Karolis Urbonas
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.
- Teacher: Kuber Deokar
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.
- Teacher: Peter Gedeck
Aim of Course:
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.
- Teacher: Anthony Babinec