Lecture slides, practice exams and online tutorials in R from a course in "how to learn from data and understand uncertainty using the ideas of probability theory and statistics" given in 2019
Curricula and Syllabi in Statistics for Economists
Part of the MIT OpenCourseWare website, this page provides access to all the materials for Introduction to Statistical Method in Economics, as taught by Herman Bennett in spring 2006. The site includes a course syllabus, details of readings, lecture notes, assignments, exams and links to related Internet resources. Users can access the course online or download the whole thing as a .zip file.
This course webpage supports an introductory module on quantitative economics as taught by Fowad Murtaza and Domenico Tabasso at the University of Essex in 2009/10. It introduces students to the methods of quantitative economics, i.e. to how data are used in economics. Beginning from an elementary level (assuming no background in statistics), the course shows how economic data can be described and analysed. The elements of probability and random variables are introduced in the context of economic applications. The probability theory enables an introduction to elementary statistical inference: parameter estimation, confidence intervals and hypothesis tests. With these foundations, students are then introduced to the linear regression model that forms a starting point for econometrics. It includes a course outline / handbook, lecture presentations, lecture notes, coursework assignments, problem sets with solutions and statistical data.
This course page supports an MBA course at the University of Portland, as taught by Todd Easton in 2008. It covers Statistical and Quantitative Analysis and presents tools for descriptive statistics and details their effective use. The page features a range of course materials - syllabus, past exams, Java applets etc. but perhaps most importantly it includes a booklet on Excel skills, with accompanying data.