This course is oriented toward US high school students. The course is divided into 10 units of study. The first five units build the foundation of concepts, vocabulary, knowledge, and skills for success in the remainder of the course. In the final five units, we will take the plunge into the domain of inferential statistics, where we make statistical decisions based on the data that we have collected.

This assignment was designed for students in the pathways introductory chemistry class and the first year seminar and aligns with the Inquiry and Problem Solving core competency. In this context, there is a focus on framing the issues (identifies and/or addresses questions and problems), evidence gathering (assembles, reviews and synthesizes evidence from several diverse sources), evidence (analyze the data to address the questions posed) and conclusions (critical thinking, reflect on the outcomes, draw conclusions and generate new knowledge). There is also a Global Learning component based on comparing data collected locally with corresponding data from other locations or countries. The assignment includes the written communication ability with a focus on "Content Development and Organization," as well as the clarity of the communication and its purpose. The overall aim of this assignment is to enhance students' conceptual learning and understanding of key issues related to society as well as their course. This assignment was developed as part of a LaGuardia Global Learning mini-grant and CUNY Experiential Learning and Research in the Classroom mini-grants.
The assignment will be scaffolded over about 3 weeks and is worth about 10% of the final grade.
To further increase the success of this assignment, instructors might want to consider the following: Use class discussions to focus on the relevance and importance of conceptual learning. In order to improve the data analysis aspect, incorporating class demonstrations of how to conduct the analysis and guide discussions about what the data means. Giving students more detailed rubrics with formal expectations of the requirements of the assignments, particularly in the written format Find ways to increase student participation in class discussions.
When this assignment has been utilized in previous semesters, students clearly displayed the capability to relate the co-curricular experiences in the data collection and its analysis to concepts and ideas covered during class. Evidence for this came from very dynamic and interactive class discussions based on air pollution as well as from the output of the written assignment, in which students were able to relate the nature, sources and chemical properties of the pollutants to their impact on the environment, health and society in general.
LaGuardia's Core Competencies and Communication Abilities
List the Program Goal(s) that this assignment targets
Global Learning based on comparing pollutant levels around the LaGuardia campus with those in other locations or countries. It is also an IPS assignment, incorporating scientific literacy and thinking, as students need to analyze the data, interpret it and reflect on the outcomes.
List the Student Learning Objective(s) that this assignment targets
Identify and apply fundamental chemical concepts and methods. Gather, analyze, and interpret data.
List the Course Objectives(s) that this assignment targets
Explore the complex connections between chemistry and society. Apply chemical principles to real world issues, including ethical aspects. Gather, analyze, and interpret data.
Write a short description of the pedagogy involved in executing this assignment
Students collect and analyze the data, interpret the results in terms of pollution levels, safety and ethics and compare with EPA standard levels and with levels in other countries.
Outside the classroom events will be organized for data collection. There will be class and group-based discussions focused on the data, its analysis and the connections to society.

I designed the course for graduate students who use statistics in their research, plan to use statistics, or need to interpret statistical analyses performed by others. The primary audience are graduate students in the environmental sciences, but the course should benefit just about anyone who is in graduate school in the natural sciences. The course is not designed for those who want a simple overview of statistics; we’ll learn by analyzing real data. This course or equivalent is required for UMB Biology and EEOS Ph.D. students. It is a recommended course for several of the intercampus graduate school of marine science program options.

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.

This open education resource (OER) contains course materials for a full semester course in Statistics. These course materials were developed by Professors Linda Weiser Friedman (Baruch College, CUNY) and Hershey H. Friedman (Brooklyn College, CUNY).

This is an introductory statistics class focused on the concepts of biological data analyses and on how statistics can help extract scientific insight from data. With the use of real examples from biology and medicine we will learn what statistic methods to use in each case and why. During the course, we will demonstrate how to carry out the calculations for the methods learned and how to implement these methods in the computer program R.

This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data.

Introductory survey of quantitative methods (QM), or the application of statistics in the workplace. Examines techniques for gathering, analyzing, and interpreting data in any number of fieldsĺÎĺ from anthropology to hedge fund management.

This is the first of two lessons/labs for teaching and learning of computer science and sociology. Either and be used on their own or they can be used in sequence, in which case this should be used first.
Students will develop CS skills and behaviors including but not limited to: learning what an API is, learning how to access and utilize data on an API, and developing their R coding skills and knowledge. Students will also learn basic, but important, sociological principles such as how poverty is related to educational opportunities in America. Although prior knowledge of CS and sociology is helpful, neither is necessary for student (or instructor) success on this two-week project. Three instructional hours per week (total of six hours over two weeks).

This file is associated with Clear-Sighted Statistics, Module 15: Two-Sample Null Hypothesis Significance Tests.

This file is associated with Clear-Sighted Statistics, Module 15: Two-Sample Null Hypothesis Significance Tests.

This file is associated with Clear-Sighted Statistics, Module 15: Two-Sample Null Hypothesis Significance Tests.