This is a brief conditional probability examplediscussing probabilities like P(A | B) using breakfast and lunch as examples.
This video talka about what is easily one of the most fundamental and profound concepts in statistics and maybe in all of mathematics. And that's the central limit theorem.
Living in a big city like New York can be very challenging. City planning is an interdisciplinary enterprise where social scientists, humanists, psychologists, scientists, statisticians, citizens, politicians, etc. come together to offer solutions to improve quality of life in the city. To find such solutions, these people need clear and reliable (qualitative and quantitative) information about specific challenges that residents and visitors face For the variety of stakeholders in the city, many different things might be considered worthy of study, depending on their interests and needs regarding, e.g., employment, financial status, family size, healthcare, mobility, and education.
For example, do you know whether your neighborhood issufficiently protected from a fire? What about other neighborhoods in the city? To what extent does a CUNY degree help a person gain employment in the City? In which ways do race or gender or sexual preference play a role in how people experience city life? Can these be quantified in dollar terms? Once you have identified a problem, write an essay that describes a question
about city life that you believe is worthy of a statistical study.
As if they are environmental engineers, student pairs are challenged to use Google Earth Pro (free) GIS software to view and examine past data on hurricanes and tornados in order to (hypothetically) advise their state government on how to proceed with its next-year budget—to answer the question: should we reduce funding for natural disaster relief? To do this, students learn about maps, geographic information systems (GIS) and the global positioning system (GPS), and how they are used to deepen the way maps are used to examine and analyze data. Then they put their knowledge to work by using the GIS software to explore historical severe storm (tornado, hurricane) data in depth. Student pairs confer with other teams, conduct Internet research on specific storms and conclude by presenting their recommendations to the class. Students gain practice and perspective on making evidence-based decisions. A slide presentation as well as a student worksheet with instructions and questions are provided.
Published by OpenStax College, Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two and fouryear colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it.
n this task, students are able to conjecture about the differences 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 appropraite graphs, particularly those of similar scale. Students are also encouraged to consider how certain measurements and observation values from one group compare in the context of the other group.
Students groups create scientific research posters to professionally present the results of their AQ-IQ research projects, which serves as a conclusion to the unit. (This activity is also suitable to be conducted independently from its unit—for students to make posters for any type of project they have completed.) First, students critically examine example posters to gain an understanding of what they contain and how they can be made most effective for viewers. Then they are prompted to analyze and interpret their data, including what statistics and plots to use in their posters. Finally, groups are given a guide that aids them in making their posters by suggesting all the key components one would find in any research paper or presentation. This activity is suitable for presenting final project posters to classmates or to a wider audience in a symposium or expo environment. In addition to the poster-making guide, three worksheets, six example posters, a rubric and a post-unit survey are provided.
This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.
This lesson combines conditional probability and combinations to determine the probability of picking a fair coin given that it flipped 4 out of 6 heads. [Probability playlist: Lesson 16 of 29]
Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise).
Students learn about the role engineers and mathematicians play in developing the perfect bungee cord length by simulating and experimenting with bungee jumping using washers and rubber bands. Working as if they are engineers for a (hypothetical) amusement park, students are challenged to develop a show-stopping bungee jumping ride that is safe. To do this, they must find the maximum length of the bungee cord that permits jumpers (such as brave Washy!) to get as close to the ground as possible without going "splat"! This requires them to learn about force and displacement and run an experiment. Student teams collect and plot displacement data and calculate the slope, linear equation of the line of best fit and spring constant using Hooke's law. Students make hypotheses, interpret scatter plots looking for correlations, and consider possible sources of error. An activity worksheet, pre/post quizzes and a PowerPoint® presentation are included.
Data Science in a Box is a fully developed curriculum for an introductory data science course in the R programming language. The curriculum contains high-quality instructional videos, slides, and activities developed by leaders in the Data Science community. The curriculum incorporates readings from two OER texts: R for Data Science and Introduction to Modern Statistics. In addition to providing instructional resources for students, the curriculum also provides guidance for faculty on implementation, pacing, and assessment of student learning. The curriculum has excellent accessibility and adaptability. It is freely available via a GitHub repository which enables all materials (slides, assignments, assessments) to be downloaded and edited. The curriculum is well organized. The videos are well developed, contain accompanying slides, and present clear and engaging examples that foster understanding and fluency with R. The online texts are well written, well organized, modular, and contain useful examples and exercises for students. The texts complement the activities well and strengthen student understanding of data science and statistics in meaningful ways.
In this lesson, students learn that sound is energy and has the ability to do work. Students discover that sound is produced by a vibration and they observe soundwaves and how they travel through mediums. They understand that sound can be absorbed, reflected or transmitted. Through associated activities, videos and a PowerPoint presentation led by the teacher, students further their exploration of sound through discussions in order to build background knowledge.
In an increasingly data-driven world, data and its use aren’t always all it’s cracked up to be. This course aims to address the critical lack of any or appropriate data in many areas where complex decisions need to be made.
For instance, how can you predict volcano activity when no eruptions have been recorded over a long period of time? Or how can you predict how many people will be resistant to antibiotics in a country where there is no available data at national level? Or how about estimating the time needed to evacuate people in flood risk areas?
In situations like these, expert opinions are needed to address complex decision-making problems. This course, aimed at researchers and professionals from any academic background, will show you how expert opinion can be used for uncertainty quantification in a rigorous manner.
Various techniques are used in practice. They vary from the informal and undocumented opinion of one expert to a fully documented and formal elicitation of a panel of experts, whose uncertainty assessments can be aggregated to provide support for complex decision making.
In this course you will be introduced to state-of-the-art expert judgment methods, particularly the Classical Model (CM) or Cooke’s method, which is arguably the most rigorous method for performing Structured Expert Judgment.
CM, developed at TU Delft by Roger Cooke, has been successfully applied for over 30 years in areas as diverse as climate change, disaster management, epidemiology, public and global health, ecology, aeronautics/aerospace, nuclear safety, environment and ecology, engineering and many others.
This is a task from the Illustrative Mathematics website that is one part of a complete illustration of the standard to which it is aligned. Each task has at least one solution and some commentary that addresses important aspects of the task and its potential use.
This is a task from the Illustrative Mathematics website that is one part of a complete illustration of the standard to which it is aligned. Each task has at least one solution and some commentary that addresses important aspects of the task and its potential use.
This is a task from the Illustrative Mathematics website that is one part of a complete illustration of the standard to which it is aligned. Each task has at least one solution and some commentary that addresses important aspects of the task and its potential use.
Students are presented with the following challenge: their new school is under construction and the architect accidentally put the music room next to the library. Students need to design a room that will absorb the most amount of sound so that the music does not disturb the library. Students use a box as a proxy for the room need to create a design that will decrease the sound that is coming from the outside of the box. To evaluate this challenge, students use a speaker within the box and a decibel meter outside the box to measure the effectiveness of their design.
This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events with applications in design.
This video defines the differences between discrete and continuous random variables, then works through examples of each.