Lecture Notes and Short Texts in Statistics for Economists

These free course materials require a login, either via Google, Facebook or a Udacity account. The course is organised into eighteen lessons, each with problem sets, and aims to cover the basics of statistical research using everyday examples. As with other MOOCs, there is a forum where learners can discuss questions.

Materials from a Spring 2022 course, including readings, assessment materials and R Studio code. "Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression."

Sixty types of data visualisation are given short explanations here, including density plots, population pyramids, chloropleth maps and Sankey diagrams. The site discusses some advantages and disadvantages of each. Each entry has a very short video clip.

This is an open online course, available freely to independent learners but with a fee for students who want feedback from an instructor. The course makes much use of online text, diagrams and interactive assignments. It is comprised of four units: Exploratory Data Analysis, Producing Data, Probability and Inference. These are subdivided into a total of twelve modules and more than two hundred "pages" of material. It is similar to the companion course on Statistical Reasoning, but with a more classical treatment of probability.

This is an open online course, available freely to independent learners but with a fee for students. The course makes much use of online text, diagrams and interactive assignments. It is comprised of four units: Exploratory Data Analysis, Producing Data, Probability and Inference. These are subdivided into a total of twelve modules and more than two hundred "pages" of material.

Not specifically aimed at economists, but this is an overview of errors and fallacies in the use of statistics for scientific inference. It presumes no prior knowledge of statistics. Base rate fallacies, underpowered tests, misinterpretation of significance, and regression to the mean are among the topics.

These free course materials require a login, either via Google, Facebook or a Udacity account. They cover "Visualizing relationships in data", "Probability", "Estimation", "Outliers and Normal Distribution", "Inference", and "Regression". The "classroom" link takes you to a large number of short YouTube videos each explaining a different step. The "Materials" link takes you to detailed, line by line transcripts which can be downloaded as PDFs. These include some formative questions. As with other MOOCs, there is a forum for learners to discuss questions arising from the material.

These case studies in data search, management and economic interpretation are aimed at first-time students of economics. They are downloadable in Word format with embedded links, to adapt, print and/or put in a virtual learning environment. They were produced by Dean Garratt and Stephen Heasell, Nottingham Trent University as part of an Economics Network project. They cover topics such as economic growth, house prices, household debt, consumption spending and government spending.

Lecture notes and assessment materials from a general undergraduate course in statistical thinking taught in 2011 and based on the Tamhane & Dunlop textbook Statistics and Data Analysis: From Elementary to Intermediate. Topics include "Summarizing and Exploring Data", "Basic Concepts of Inference", "Inferences for Proportions and Count Data", "Similar Linear Regression and Correlation", and"Multiple Linear Regression"

Detailed lecture notes in PDF, reading list, past exams, and assignments from a 2009 course based on Larsen and Marx. Introduction to Mathematical Statistics and Its Applications. Available in Turkish as well as English.

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.

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.

The Chance project aims to help teachers and lecturers teach a basic understanding of probability and statistics. This page hosts examples of probabilistic and statistical reasoning, with links to related activities, simulations, videos, and data sets. Related links are also included.

Thirteen PDF files on this page each contain detailed lecture notes on: Sets and Subsets, Numbers, Limits, Derivatives, Maxima and Minima, Geometric Growth, Taylor's Theorem, The Binomial Theorem, Exponential and Logistic Growth, Bivariate Optimisation, Constrained Optimisation, Matrix Algebra 1, and Matrix Algebra 2.

School of Data is an extensive site aiming "to empower civil society organizations, journalists and citizens with the skills they need to use data effectively." The site includes many textual "courses" explaining topics such as Exploring and Understanding Data, Common Misconceptions, Data Cleaning, or Data Journalism. There are also "recipes" explaining how to do tasks with specific tools. Often the tools are simple and freely available, such as Google Spreadsheets. The content is freely reusable and makes copious use of graphs and illustrations.