This textbook is part of the OpenIntro Statistics series and offers complete coverage of the high school AP Statistics curriculum. Real data and plenty of inline examples and exercises make this an engaging and readable book. Links to lecture slides, video overviews, calculator tutorials, and video solutions to selected end of chapter exercises make this an ideal choice for any high school or Community College teacher. In fact, Portland Community College recently adopted this textbook for its Introductory Statistics course, and it estimates that this will save their students $250,000 per year. Find out more at: openintro.org/ahss

View our video tutorials here:
openintro.org/casio
openintro.org/TI

This course is an arithmetic course intended for college students, covering whole numbers, fractions, decimals, percents, ratios and proportions, geometry, measurement, statistics, and integers using an integrated geometry and statistics approach. The course uses the late integers model—integers are only introduced at the end of the course.

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.

The applets in this section of Statistical Java allow you to see how levels of confidence are achieved through repeated sampling. The confidence intervals are related to the probability of successes in a Binomial experiment.

The applets in this section allow you to see how the common Xbar control chart is constructed with known variance. The Xbar chart is constructed by collecting a sample of size n at different times t.

The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples.

Welcome to data journalism. The main goal of this course is to expand your ability to report and tell stories using data. You will use these tools to discover trends in data. You will learn how to create and publish graphics and maps. It’s hard work but it is a lot of fun and very rewarding.

We have some basic goals for you to reach in this class. By the end of the semester, we want you to have basic proficiency and independence with data analysis. We want you to be able to write about data clearly, using the Associated Press style as a benchmark. We want you to be able to find and download a dataset, clean it up, visualize it.

You’ll get a taste of modern data journalism through Google Sheets and programming in R, a statistics language. You’ll be challenged to think programmatically while thinking about a story you can tell to readers in a way that they’ll want to read. Combining them together has the power to change policy and expose injustice.

This book is the collection of class materials compiled by various data journalism professors around the country: Matt Waite at the University of Nebraska-Lincoln’s College of Journalism and Mass Communications and Sarah Cohen of Arizona State University. This version was rewritten by Rob Wells, building on work by Sean Mussenden and Derek Willis, at the University of Maryland Philip Merrill College of Journalism.

There’s some things you should know about it:
- It is free for students.
- The topics will remain the same but the text is going to be constantly tinkered with.
- What is the work of the authors is copyright Rob Wells 2024, Sean Mussenden and Derek Willis 2022, Matt Waite 2020 and Sarah Cohen 2022.

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.

Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring.The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs.

This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports.

This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB in particular.

This course provides graduate students in the sciences with an intensive introduction to applied statistics. Topics include descriptive statistics, probability, non-parametric methods, estimation methods, hypothesis testing, correlation and linear regression, simulation, and robustness considerations. Calculations will be done using handheld calculators and the Minitab Statistical Computer Software.

This course covers descriptive statistics, the foundation of statistics, probability and random distributions, and the relationships between various characteristics of data. Upon successful completion of the course, the student will be able to: Define the meaning of descriptive statistics and statistical inference; Distinguish between a population and a sample; Explain the purpose of measures of location, variability, and skewness; Calculate probabilities; Explain the difference between how probabilities are computed for discrete and continuous random variables; Recognize and understand discrete probability distribution functions, in general; Identify confidence intervals for means and proportions; Explain how the central limit theorem applies in inference; Calculate and interpret confidence intervals for one population average and one population proportion; Differentiate between Type I and Type II errors; Conduct and interpret hypothesis tests; Compute regression equations for data; Use regression equations to make predictions; Conduct and interpret ANOVA (Analysis of Variance). (Mathematics 121; See also: Biology 104, Computer Science 106, Economics 104, Psychology 201)

Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.

This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students. Our motivation for writing it is twofold: 1.) to provide a low-cost alternative to many existing popular textbooks on the market; and 2.) to provide a quality textbook on the subject with a focus on the core material of the course in a balanced presentation.

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

(1) Statistics is an applied field with a wide range of practical applications.

(2) You don't have to be a math guru to learn from interesting, real data.

(3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

Introduction to "soft" consumer research methods, useful for getting quick customer input into decisions on product design and development, strategic positioning, advertising, and branding. Covers interview techniques, observational methods, Voice of the Customer, focus groups, and analyses suitable for qualitative data. Introduces new information-gathering methods in development at MIT.

This course presents a unique and challenging perspective on the causes of human disease and mortality. The course focuses on analyses of major causes of mortality in the US since 1900: cancer cardiovascular and cerebrovascular diseases, diabetes, infectious diseases. Students create analytical models to derive estimates for historically variant population risk factors and physiological rate parameters, and conduct analyses of familial data to separately estimate inherited and environmental risks. The course evaluates the basic population genetics of dominant, recessive and non-deleterious inherited risk factors.

Core subject for students majoring in management science. Surveys individual and social psychology and organization theory interpreted in the context of the managerial environment. Laboratory involves projects of an applied nature in behavioral science. Emphasizes use of behavioral science research methods to test hypotheses concerning organizational behavior. Instruction and practice in communication include report writing, team decision-making, and oral and visual presentation.