Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise)

The video resource "Crash Course Statistics Preview" is included in the "Statistics" course from the resources series of "Crash Course". Crash Course is a educational video series from John and Hank Green.

In an increasingly data-driven world, data and its use aren’t always all it’s cracked up to be. This course aims to address the critical lack of any or appropriate data in many areas where complex decisions need to be made.

For instance, how can you predict volcano activity when no eruptions have been recorded over a long period of time? Or how can you predict how many people will be resistant to antibiotics in a country where there is no available data at national level? Or how about estimating the time needed to evacuate people in flood risk areas?

In situations like these, expert opinions are needed to address complex decision-making problems. This course, aimed at researchers and professionals from any academic background, will show you how expert opinion can be used for uncertainty quantification in a rigorous manner.

Various techniques are used in practice. They vary from the informal and undocumented opinion of one expert to a fully documented and formal elicitation of a panel of experts, whose uncertainty assessments can be aggregated to provide support for complex decision making.

In this course you will be introduced to state-of-the-art expert judgment methods, particularly the Classical Model (CM) or Cooke’s method, which is arguably the most rigorous method for performing Structured Expert Judgment.

CM, developed at TU Delft by Roger Cooke, has been successfully applied for over 30 years in areas as diverse as climate change, disaster management, epidemiology, public and global health, ecology, aeronautics/aerospace, nuclear safety, environment and ecology, engineering and many others.

Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables.

This course blends Introductory Statistics from OpenStax with other OER to offer a first course in statistics intended for students majoring in fields other than mathematics and engineering. This course assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.The foundation of the OpenStax text is Collaborative Statistics, by Barbara Illowsky and Susan Dean. The development choices for this textbook were made with the guidance of many faculty members who are deeply involved in teaching this course. These choices led to innovations in art, terminology, and practical applications, all with a goal of increasing relevance and accessibility for students. We strove to make the discipline meaningful, so that students can draw from it a working knowledge that will enrich their future studies and help them make sense of the world around them.

This video is from the Khan Academy subject of Math on the topic of Probability and statistics and it covers Estimating the line of best fit exercise.

Paul Andersen shows you how to graph data by hand. He explains the required elements of a scatter plot with a best fit line. He shows you how to properly scale and label the axes.

Paul Andersen shows you how to create a scatter plot with a best fit line in Microsoft Excel.

The video resource "Henrietta Lacks, the Tuskegee Experiment, & Ethical Data Collection: Crash Course Statistics #12" is included in the "Statistics" course from the resources series of "Crash Course". Crash Course is a educational video series from John and Hank Green.

This video is from the Khan Academy subject of Math on the topic of Probability and statistics and it covers Intersection and union of sets.

The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl