This introductory-level course teaches the fundamental concepts of statistics and the logic of statistical reasoning. Designed for students with no prior knowledge of statistics, the only prerequisite is basic algebra. It includes a classical treatment of probability and practical skills for choosing, generating, and interpreting descriptive and inferential statistical methods. The course aims to help students appreciate the diverse applications of statistics and its relevance to their lives and fields of study. This is an "Open & Free" version, meaning all course materials, including text, simulations, case studies, interactive exercises, and labs, are available for independent learners. However, scored tests and instructor tools are omitted.
The course is structured around four main units, covering Exploratory Data Analysis, Producing Data, Probability, and Inference. The "Probability and Statistics" version features a classical treatment of probability, distinct from the "Statistical Reasoning" version.
This "Open & Free" course is designed for independent learners and does not have formal admission requirements or instructor support.
Carnegie Mellon University's standard tuition and fees apply to degree programs, not to these open educational resources.
The "Open & Free" version of this course is available at no cost for independent learners. Standard tuition and fees apply only to degree programs, not to these open educational resources.
No prior statistics knowledge is needed. The only prerequisite for this course is basic algebra.
This course is "Open & Free" for independent learners, meaning you can access the materials directly without a formal application process. Simply navigate to the course page on the Carnegie Mellon University Open Learning Initiative (OLI) website and click the "ENTER OPEN & FREE COURSE" button.
This is a self-paced, online course designed for independent learning. There is no fixed duration.
While this course does not lead to a degree, the skills acquired are valuable in fields like data science, research, finance, and engineering.