Students prepare for the exercise by reading about normal faults in the structural geology textbook. The class is divided into groups of 3-5 students. Each group is given two clear plastic shoe boxes, each of which has one end cut off so that one box slides lengthwise into the other box. Students are charged with running three extensional sandbox experiments during the class period, in which they fill the shoe box with sand having different physical properties (ex. grain size, clay content). The groups have access to materials (such as Saran plastic wrap) that can be used to line the boxes and provide different physical properties along the basal detachment. Students are assigned three main tasks: to explore a variety of physical parameters that may influence the characteristics of normal faults in analog models, to observe typical geometry and kinematics of normal fault development in an extensional setting, and to draw inferences and form hypotheses about the general controls on normal faulting. Students take notes on the conditions of each experiment, then write brief descriptions of geometric characteristics of the faults. They are asked to evaluate which observations appear to be repeatable from one experiment to another. After the groups have finished running experiments and taking notes, the class reassembles for an instructor-led brainstorming session. The instructor makes a list of student-generated observations, key parameters, and possible inferences on the board. The instructor leads the class in a discussion that addresses issues such as the key characteristics of normal faults, kinematics, mechanical principles, predictability of results, and the applications of analog models.

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An investigation of changes in polar regions using Google Earth.

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Statistics on Nuclear energy and how it is used around the world. How much nuclear power is used and where.

Using State Facts for Students, a data access tool from the U.S. Census Bureau, students will explore data about their state and voice their opinions on how the population has changed over time. Students will work in small groups to share their opinions, practicing oral communication and small-group discussion skills.

Estimating nutrient loads is a critical concept for students studying water quality in a variety of environmental settings. Many of these students will be asked to assess the impacts of a proposed anthropogenic activities on human water resources and/or ecosystems as part of their future careers. This module has students explore factors contributing to the actual loads of nitrogen that are transmitted down streams. Nitrogen is a key water quality contaminant contributing to surface water quality issues in fresh, salt and estuarine environments. Students will utilize real-time nitrate data from the US Geological Survey to calculate nitrate loads for several locations and investigate the interplay of concentration and discharge that contributes to the calculated loads.

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Provenance: Used with permission from the Chesapeake Bay Program: https://www.chesapeakebay.net/
Reuse: If you wish to use this item outside this site in ways that exceed fair use (see http://fairuse.stanford.edu/) you must seek permission from its creator.
The Chesapeake Bay waters receive input from rivers and streams from areas of Washington D.C, Maryland, Delaware, Virginia, West Virginia, and some parts of New York and Pennsylvania. Historically, humongous amounts of water pollution from nutrients discharged from these locations have reportedly occurred in the waters of the Chesapeake Bay region, such that it was entered into the list of the "Clean Water Act Section 303(d): Impaired Waters and Total Maximum Daily Loads (TMDLs)," compiled by the EPA. Water impairment occurs when a lake, river, or stream fails to meet specific water quality standards, according to its classification and intended use.
According to the Chesapeake Bay Program, established in 1983 to reduce pollution and restore the ecosystem, "Plants and animals need nutrients to survive. But when too many nutrients enter waterways, they fuel the growth of algae blooms and create conditions that are harmful to underwater life." Source: Chesapeake Bay Program: Learn the Issues.

Les vidéos 1080 ont pour objectif de vulgariser le savoir scientifique à destination des étudiants, des journalistes, des chercheurs de tous domaines et du grand public.

The Shiny@UCLouvain platform is a repository for sharing teaching resources created in Shiny for the Catholic University of Louvain (UCLouvain) with the aim of teaching statistics with interactive apps. The list of apps (Inference, Probability, Distributions, Central Limit Theorem, Confidence intervals, Hypothesis test on the mean, Bootstrap confidence intervals, Design of Experiments , ...) associated with the RShiny@UCLouvain platform can be found at https://sites.uclouvain.be/RShiny/main.html . The source code of the apps can be found at https://forge.uclouvain.be/rshiny_uclouvain .

Cette ressource propose un recueil de diapos utilisées à l'UCL pour un cours de probabilités et statistique destiné à des étudiants en sciences humaines. Ce cours est précédé, à l'UCL, par un cours de statistique descriptive. N'hésitez pas à contacter l'auteur - bernadette govaerts@uclouvain.be - qui dispose de ressources complémentaires sur le sujet (énoncés d'exercices, jeux de données...).
Contenu :
- Eléments de Probabilités
° P1 : Introduction
° P2 : Calcul de probabilités sur des événements
° P3 : Variables aléatoires : généralités et lois classiques
° P4 : Théorème central limite et combinaisons de variables aléatoires
-Inférence pour une et deux variables
° I1 : Principes de l’inférence statistique (rappels)
° I2 : Inférence sur les paramètres d’UNE variable quantitative normale
° I3 : Tests sur les paramètres d’une variable quantitative normale observée sur deux groupes indépendants ou pairés
° I4 : Inférence sur les paramètres d’une variable catégorielle
° I5 :Tests d’homogénéité et d’indépendance pour deux variables catégorielles
° I6 : Tests non paramétriques sur une ou deux valeurs centrales
° I7: Inférence sur un ou deux coefficients de corrélation
° I8 : Puissance d'un test, calcul de taille d'échantillon

Tables de probabilités et de statistiques : Table de calcul de probabilité pour la loi binomiale Table de calcul de probabilité pour la loi normale Table des quantiles de la v.a. Chi-Carré Table des quantiles de la v.a. Fisher Table des quantiles de la v.a. Normale réduite Table des quantiles de la v.a. Student

Cette ressource propose une série de vidéos permettant un apprentissage autonomne de SPSS ainsi que des fichiers utiles liés. N'hésitez pas à contacter le premier auteur - bernadette govaerts@uclouvain.be - qui dispose de ressources complémentaires sur le sujet (énoncés d'exercices, jeux de données...)

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

The Data Science curriculum assumes the student has taken high school math and statistics.

In this task, students are able to conjecture about the differences and similarities in the two groups from a strictly visual perspective and then support their comparisons with appropriate measures of center and variability. This will reinforce that much can be gleaned simply from visual comparison of appropriate graphs, particularly those of similar scale.

Data modeling activity using oil reserve and consumption data. Students predict when oil reserves meet or exceed reserves.

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This activity uses the song There was an Old Lady to help students understand probability.

Students use the height and radius of Olympus Mons to estimate its volume. They then propose a method to estimate the volume of lava that has erupted over from the Hawaiian hotspot over time. I then show them a graph of the cumulative volcanic volume as a function of distance from Kilauea (from Clague and Dalrymple). They compare these volumes and also consider the possibility that some of the lava erupted from the Hawaiian hotspot has been subducted.

(Note: this resource was added to OER Commons as part of a batch upload of over 2,200 records. If you notice an issue with the quality of the metadata, please let us know by using the 'report' button and we will flag it for consideration.)

The Open for Antiracism (OFAR) Program – co-led by CCCOER and College of the Canyons – emerged as a response to the growing awareness of structural racism in our educational systems and the realization that adoption of open educational resources (OER) and open pedagogy could be transformative at institutions seeking to improve. The program is designed to give participants a workshop experience where they can better understand anti-racist teaching and how the use of OER and open pedagogy can empower them to involve students in the co-creation of an anti-racist classroom. The capstone project involves developing an action plan for incorporating OER and open pedagogy into a course being taught in the spring semester. OFAR participants are invited to remix this template to design and share their projects and plans for moving this work forward. 

OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League

Materials created by Larry Shrewsbury when he piloted the open source textbook “OpenIntro Statistics” during Fall 2016 through Spring 2017. These are MS Word documents so you can edit them to suit you.

MTH 243: Emphasizes the basic concepts and techniques of probability, descriptive, and inferential statistics. Topics include describing the distribution of data graphically and numerically, standard scores, normal distribution, empirical rule, sampling distributions, confidence intervals, hypothesis testing of both one and two populations, and linear regression. Introduces appropriate technology to display and analyze data.

MTH 244: Presents an assortment of tools from inferential statistics with an emphasis on applications. Reviews the concepts of hypothesis testing and confidence intervals. Introduces probability distributions of test statistics for various inferential statistical problems. Includes Analysis of Categorical Data (Chi-Square Goodness of Fit Test), Analysis of Variance (ANOVA), Nonparametric Statistics, and a brief introduction to Multiple Linear Regression. Applies the concepts and procedures with appropriate software tools for data analysis.

This sample shell is produced by the California Community Colleges CVC-OEI to support faculty in the use of Open Educational Resources and development of courses aligned to the OEI Course Design Rubric. The shell may be used for online, hybrid, &/or face-to-face classes. The shell is available for all faculty, not just those faculty in the CCC system. The team producing this shell includes Helen Graves, Liezl Madrona, Cyrus Helf, Nicole Woolley & Barbara Illowsky. If you are having challenges importing the shell, here are some steps to take. (1) Create an empty shell in your sandbox. (2) Import the Canvas Commons course into your shell. (3) Adapt the content as you wish. (4) If all else fails, contact your college IT person or Canvas administrator.