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.
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:
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.
- Teacher: Ken Strasma
Aim of Course:
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.
- Teacher: Michael Borenstein
- Teacher: Hannah Rothstein
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.
- Teacher: Robert LaBudde
Aim of Course:
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
- Teacher: Karen Schmidt
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.
- Teacher: Cliff Ragsdale
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Peter Bruce
- Teacher: Kuber Deokar
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 teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
- Teacher: Jen Golbeck
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.
- Teacher: Anthony Babinec
Aim of Course:
The purpose of this online course is to teach you how to extract data from a relational database using SQL, so that you can perform statistical operations. The focus is on structuring queries to extract structured data (not on building databases or methods of handling big data).
- Teacher: Ben Baumer
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: Kuber Deokar
- 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: Kuber Deokar
- Teacher: Inbal Yahav
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
This course provides an introduction to the Python programming language for the complete beginner. We will set up a Python programming environment from download to writing programs, taking small steps along the way. The programming environment will feature the Jupyter notebook interface.
- Teacher: Stan Blank
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.
- Teacher: Abhaya Indrayan
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: Michelle Everson
Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Meena Badade
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.
- Teacher: Paul Murrell
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.
- Teacher: Galit Shmueli