This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.

“The Art of the Probable” addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world.
Our course therefore aims to broaden students’ appreciation for and understanding of how literature interacts with – both reflecting upon and contributing to – the scientific understanding of the world. We are just as centrally committed to encouraging students to regard imaginative literature as a unique contribution to knowledge in its own right, and to see literary works of art as objects that demand and richly repay close critical analysis. It is our hope that the course will serve students well if they elect to pursue further work in Literature or other discipline in SHASS, and also enrich or complement their understanding of probability and statistics in other scientific and engineering subjects they elect to take.

Student groups are given captioned photographs of the Chernobyl Nuclear Power Plant facility and surrounding towns taken before and 28 years after the 1986 disaster. Based on the captions and clues in the images, they arrange them in sequential order. While viewing the completed sequence of images, students reflect on what it might have been like to be there, and ask themselves: what were people thinking, doing and saying at each point? This activity assists students in gaining an understanding of how devastating nuclear meltdowns can be, which underscores the importance of responsible engineering. It is recommended that this activity be conducted before the associated lesson, Nuclear Energy through a Virtual Field Trip.

This is an online course in experimentation as a method of the empirical social sciences, directed at science newcomers and undergrads. We cover topics such as:
- How do we know what’s true?
- How can one recognize false conclusions?
- What is an experiment?
- What are experiments good for, and what can we learn from them?
- What makes a good experiment and how can I make a good experiment?

The aim of the course is to illustrate the principles of experimental insight. We also discuss why experiments are the gold standard in empirical social sciences and how a basic understanding of experimentation can also help us deal with questions in everyday life.

But it is not only exciting research questions and clever experimental set-ups that are needed for experiments to really work well. Experiments and the knowledge gained from them should be as freely accessible and transparent as possible, regardless of the context. Only then can other thinkers and experimenters check whether the results can be reproduced. And only then can other thinkers and experimenters build their own experiments on reliable original work. This is why the online course Open for Insight also discusses how experiments and the findings derived can be developed and communicated openly and transparently.

This is a critical thinking evaluation report that may be used as an assignment resource, free of charge, by any educator for the purposes of higher learning. It has been utilized as a resource in:2022-2023 UNC Undergraduate Research Program. Fostering Critical Thinking in Human Motion Analysis. Brown, J., Chandler, R., Fiaud, V., and Armitano, C. This resource was recently featured in a presentation at the National Association for Kinesiology in Higher Education in January of 2024 and disseminated for public use. For more information, please email:jjbrown@ecsu.edu

This is a textbook for teaching data science using the Python programming language at a general first year undergraduate level.

This is a textbook for teaching data science using the R programming language at a general first year undergraduate level.

Developed for students in advanced ESL/ELL classes as well as for native English speakers with low reading skills, this group lesson focuses on the formulation of inferences, and the relevant explicit details which support each inference. The initial presentation highlights the skill of making inferences in a real-world context, then transitions to the literary context. Students read selected chapters of The House on Mango Street, by Sandra Cisneros, a core text in many junior high and high school curricula across the United States. The students read out loud. Then, in groups they formulate inferences based on what they have read. Using sentence strips, they summarize the inference as well as cite the textual details which support each inference.

In this unit you will learn about making inferences and the ways that active readers use this skill to improve both engagement and comprehension.

This unit contains a lesson, a link to a video, a short story, and a practice assignment.

This resource was created as part of a Developmental Reading course redesign project, with contributions from Theresa Love and David Pontious and support from an Open Oregon Educational Resources grant.

The reading strategy known as inferring is also one of the six basic process skills in science. How to apply the strategy in teaching K-5 reading and science is explained in this article from the free, online magazine Beyond Weather and the Water Cycle. The magazine is designed to prepare elementary teachers to teach climate science concepts while integrating inquiry-based science and literacy instruction.

This is the website for Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin. Introduction to Modern Statistics, which we’ll refer to as IMS going forward, is a textbook from the OpenIntro project.
—
Copyright © 2021.
First Edition.
Version date: June 27, 2021.

This textbook and its supplements, including slides, labs, and interactive tutorials, may be downloaded for free at
openintro.org/book/ims.

This textbook is a derivative of OpenIntro Statistics 4th Edition and Introduction to Statistics with Randomization and Simulation 1st Edition by Diez, Barr, and Çetinkaya-Rundel, and it’s available under a Creative Commons Attribution-ShareAlike 3.0 Unported United States License. License details are available at the Creative Commons website: creativecommons.org.

Source files for this book may be found on GitHub at github.com/openintrostat/ims.

This social media literacy unit introduces students to foundational skills in analyzing images and social media posts. It also reenforces critical thinking questions that can be applied to various forms of media. This unit was taught to 9th grade students but is easily adaptible to a range of secondary classrooms. It was also taught in conjunction with another unit focused on social media platforms and content.

The goal of this activity is to understand how techniques of persuasion (including background, supporting evidence, storytelling and the call to action) are used to develop an argument for or against a topic. Students develop an environmental case study for presentation and understand how a case study is used as an analysis tool.

Proponents of preregistration argue that, among other benefits, it improves the diagnosticity of statistical tests. In the strong version of this argument, preregistration does this by solving statistical problems, such as family-wise error rates. In the weak version, it nudges people to think more deeply about their theories, methods, and analyses. We argue against both: the diagnosticity of statistical tests depend entirely on how well statistical models map onto underlying theories, and so improving statistical techniques does little to improve theories when the mapping is weak. There is also little reason to expect that preregistration will spontaneously help researchers to develop better theories (and, hence, better methods and analyses).

Different approaches to teaching the reading comprehension strategy of inferring in K-5 classrooms are identified in this article. The article appears in the free, online magazine Beyond Weather and the Water Cycle, which is structured around the essential principles of climate science.

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

This resource was created by Janice Nichols, in collaboration with Lynn Bowder, as part of ESU2's Mastering the Arts project. This project is a four year initiative focused on integrating arts into the core curriculum through teacher education and experiential learning.

6.821 teaches the principles of functional, imperative, and logic programming languages. Topics covered include: meta-circular interpreters, semantics (operational and denotational), type systems (polymorphism, inference, and abstract types), object oriented programming, modules, and multiprocessing. The course involves substantial programming assignments and problem sets as well as a significant amount of reading. The course uses the Scheme+ programming language for all of its assignments.