Developmental and General Education Statistics (FITW MMRI)

Developed by the First in the World Maryland Mathematics Reform Initiative to provide a collaborative space for faculty interested in finding and developing OERs for statistics.
2 members | 11 affiliated resources

All resources in Developmental and General Education Statistics (FITW MMRI)

Introduction to Statistics

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This course blends Introductory Statistics from OpenStax with other OER to offer a first course in statistics intended for students majoring in fields other than mathematics and engineering. This course assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.The foundation of the OpenStax text is Collaborative Statistics, by Barbara Illowsky and Susan Dean.  The development choices for this textbook were made with the guidance of many faculty members who are deeply involved in teaching this course. These choices led to innovations in art, terminology, and practical applications, all with a goal of increasing relevance and accessibility for students. We strove to make the discipline meaningful, so that students can draw from it a working knowledge that will enrich their future studies and help them make sense of the world around them.

Material Type: Full Course, Textbook

Introductory Statistics

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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.

Material Type: Textbook

Authors: Douglas Shafer, Zhiyi Zhang

Probability and Statistics

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Probability and Statistics, besides being a key area in the secondary schools’ teaching syllabuses, it forms an important background to advanced mathematics at tertiary level. Statistics is a fundamental area of Mathematics that is applied across many academic subjects and is useful in analysis in industrial production. The study of statistics produces statisticians that analyse raw data collected from the field to provide useful insights about a population. The statisticians provide governments and organizations with concrete backgrounds of a situation that helps managers in decision making. For example, rate of spread of diseases, rumours, bush fires, rainfall patterns, and population changes.

Material Type: Module

Author: Paul Chege

Introductory Statistics

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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.

Material Type: Textbook

Authors: Barbara Ilowsky, Susan Dean

OpenIntro Statistics

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OpenIntro Statistics strives to be a complete introductory textbook of the highest caliber. Its core derives from the classic notions of statistics education and is extended by recent innovations. The textbook meets high quality standards and has been used at Princeton, Vanderbilt, UMass Amherst, and many other schools. We look forward to expanding the reach of the project and working with teachers from all colleges and schools.

Material Type: Textbook

Authors: Christopher Barr, David Diez, Mine Cetinkaya-Rundel

Introduction to Statistics

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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)

Material Type: Full Course

Introduction to Statistics (MATH 146)

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The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl

Material Type: Full Course, Homework/Assignment, Lecture Notes, Lesson Plan, Syllabus

Introductory Statistics with Randomization and Simulation First Edition

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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.

Material Type: Textbook

Authors: Christopher Barr, David Diez, Mine Çetinkaya-Runde

Introduction to Probability and Statistics 

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The aim of the course is to equip students with basic knowledge in probability and statistics needed for their studies in ACS. In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Probability and Statistics helps computer science students solve problems and make decisions in uncertain conditions, compute probabilities and forecasts, and evaluate performance of computer systems and networks. At the end of the course, students should be able to apply Probability & Statistics in the context of ACS. Indeed, they will be able to use statistical concepts, probabilistic calculations, methods of observations, sampling techniques, analysis and classification of variables in interpreting data, and inferring design variables results.

Material Type: Module

Author: Nafy Aidara