This model-eliciting activity challenges students to operationally define a construct (study effectiveness). Students are given a survey to review that rates different aspects of study behaviors. They are then given a set of data for a few students and asked to use their scores to determine an index of study effectiveness. After determining a method, they are then asked to use this index to put five students in rank order according to their scores on study effectiveness. Students write a report explaining the method they used to determine these scores and how they produced their ratings.

This set of assignments exposes students to statistics and data pertaining to economic wellbeing over time across racial (black-white) categories in the U.S.

Provides teaching tips, information on common errors, differentiated instruction, enrichment, and problem solving for teachers to use with the CK-12 Middle School Math - Grade 6, Student Edition.

This unit is the first in the MSXR209 series of five units that introduce the idea of modelling with mathematics. This unit centres on a mathematical model of how pollution levels in the Great Lakes of North America vary over a period of time. It demonstrates that, by keeping the model as simple as possible extremely complex systems can be understood and predicted.

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.

Methylmercury contamination within fish populations is an important toxin that affect human, animal, and environmental health, serving as a carcinogen (cancer-causing agent) and endocrine-disruptor (compounds that in some way alter the signaling of the hormone system. The impacts of exceeding safe dietary methylmercury levels were tragically made clear in Ontario, Canada, where a First Nations community in Grassy Narrows are living with the consequences of methylmercury poisoning in the fish supply. The fish were contaminated due to the dumping of mercury in the traditional waterways of the First Nation community. In 2016, there were highly publicized protests in Muskrat Falls, Labrador, Canada, where the Inuit people raised direct concerns about the potential for a proposed Nalcor Energy hydroelectric dam, to increase mercury levels in fish in those waters, which are an integral part of their traditional diet. Despite significant protests, the project was completed in 2019 and 41 km were flooded. This module uses these real-world examples as a jumping-off point for exercises that will guide case-study driven discussion on mathematical, biological and ethical concerns.

Methylmercury contamination within fish populations is an important toxin that affect human, animal, and environmental health, serving as a carcinogen (cancer-causing agent) and endocrine-disruptor (compounds that in some way alter the signaling of the hormone system. The impacts of exceeding safe dietary methylmercury levels were tragically made clear in Ontario, Canada, where a First Nations community in Grassy Narrows are living with the consequences of methylmercury poisoning in the fish supply. The fish were contaminated due to the dumping of mercury in the traditional waterways of the First Nation community. In 2016, there were highly publicized protests in Muskrat Falls, Labrador, Canada, where the Inuit people raised direct concerns about the potential for a proposed Nalcor Energy hydroelectric dam, to increase mercury levels in fish in those waters, which are an integral part of their traditional diet. Despite significant protests, the project was completed in 2019 and 41 km were flooded. This module uses these real-world examples as a jumping-off point for exercises that will guide case-study driven discussion on mathematical, biological and ethical concerns.

This resource consists of a Java applet and expository text. The applet is a simulation of the Monty Hall experiment: a car is behind one door, goats are behind the other two doors. The player chooses a door and then the host opens another door. The player is given the option of switching to the remaining door. The stochastic behavior of the host and the probability of the player switching can be specified.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

Non-significant results are less likely to be reported by authors and, when submitted for peer review, are less likely to be published by journal editors. This phenomenon, known collectively as publication bias, is seen in a variety of scientific disciplines and can erode public trust in the scientific method and the validity of scientific theories. Public trust in science is especially important for fields like climate change science, where scientific consensus can influence state policies on a global scale, including strategies for industrial and agricultural management and development. Here, we used meta-analysis to test for biases in the statistical results of climate change articles, including 1154 experimental results from a sample of 120 articles. Funnel plots revealed no evidence of publication bias given no pattern of non-significant results being under-reported, even at low sample sizes. However, we discovered three other types of systematic bias relating to writing style, the relative prestige of journals, and the apparent rise in popularity of this field: First, the magnitude of statistical effects was significantly larger in the abstract than the main body of articles. Second, the difference in effect sizes in abstracts versus main body of articles was especially pronounced in journals with high impact factors. Finally, the number of published articles about climate change and the magnitude of effect sizes therein both increased within 2 years of the seminal report by the Intergovernmental Panel on Climate Change 2007.

Lesson introduces normal distribution and how it relates to standard deviation. It includes opportunities for students to notice key features of normal distribution graphs, reviews the concept of standard distribution and the empirical rule, and provides an interactive activity where they can practice adjusting normal distribution curves based on a different standard deviation or mean.

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

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.

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 .

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.

Test, quiz, and assignment problem sets that go with the OpenStax Introduction to Statistics Textbook

PowerPoint Slides to accompany Chapter 12 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 1 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 2 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.