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Recently added materials to MERLOTCopyright (C) 2018 MERLOT Some Rights ReservedThu, 12 Apr 2018 03:42:59 GMTMERLOThttps://www.merlot.org/merlot/images/merlot_column.png
https://www.merlot.org/
-1-1Advanced High School Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1375024
<p>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</p>Thu, 12 Apr 2018 03:42:59 GMTDavid Diez; Mine Cetinkaya-Rundel; Christopher Barr; Leah Dorazio OpenIntro; Duke University; Yale School of Management; SF University High SchoolThink Stats: Probability and Statistics for Programmers
https://www.merlot.org/merlot/viewMaterial.htm?id=1370262
<p><em>Think Stats</em> is an introduction to Probability and Statistics for Python programmers.</p>
<ul>
<li><em>Think Stats </em>emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.</li>
<li>If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.</li>
</ul>Tue, 27 Feb 2018 23:30:38 GMTAllen B. Downey Franklin W. Olin College of EngineeringThink Bayes: Bayesian Statistics Made Simple
https://www.merlot.org/merlot/viewMaterial.htm?id=1370247
<p><em>Think Bayes</em> is an introduction to Bayesian statistics using computational methods.</p>
<p>The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.</p>
<p>Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.</p>
<p>I think this presentation is easier to understand, at least for people with programming skills. It is also more general because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.</p>Tue, 27 Feb 2018 23:03:11 GMTAllen B. Downey Franklin W. Olin College of EngineeringB.C. Campus Open Education: Open Textbook Self-Publishing Guide
https://www.merlot.org/merlot/viewMaterial.htm?id=1368281
<p>B.C. Campus Open Education: Open Textbook Self-Publishing Guide is a practical reference/development tool for individuals or groups wanting to write and publish an open textbook. This guide provides details on the preparation, planning, writing, and publication of an open textbook as well as the importance of editing and proofreading, Copyright, open copyright licenses, and the differences between citation and attribution are discussed. The Self-Publishing Guide by Lauri Aesoph is licenced under a CC-BY 4.0 International Licence, except where otherwise noted.</p>Wed, 21 Feb 2018 02:55:09 GMTLauri M. Aesoph B.C. Campus: Victoria, B.C.Demographic Statistics Methods And Measures In Demography
https://www.merlot.org/merlot/viewMaterial.htm?id=1358386
<p>The book is an introduction to advanced demographic techniques and has been designed for researchers and students of demography and statistics as well as social scientists and health workers.</p>Fri, 12 Jan 2018 01:57:27 GMTNicholas N.N. Nsowah-Nuamah Regent University College of Science and TechnologyIntroductory Probability Theory A first Course in Probability Theory – Volume I
https://www.merlot.org/merlot/viewMaterial.htm?id=1357824
<p>Introductory Probability Theory is volume one of the book entitles “A First Course in Probability Theory”. It is primarily intended for undergraduate students of Statistics and mathematics. It can, however, be used by students of Social Sciences and mathematics-related courses.</p>
<p>This volume covers the basic theory of probability in a simple yet easily comprehensible manner. It deals with the basic mathematical tools for the understanding of probability, such as, the set theory and the counting principles, the concept of probability, basic probability calculus, laws and theorems, the random variable, its probability distribution and numerical characterization. Determination of the central and non-central location of distributions as well as their spread are extensively discussed. Moments and moment-generating functions have also been extensively covered. </p>
<p>The book has a large number of motivating solved examples. It has a large number of exercises at the end of each chapter for students’ practice.</p>Wed, 10 Jan 2018 02:23:51 GMTNicholas N.N. Nsowah-Nuamah Regent University CollegeIntroductory Nonparametrics
https://www.merlot.org/merlot/viewMaterial.htm?id=1357711
<p>Introductory Nonparametrics gives a gentle introduction to nonparametric hypothesis testing. It describes some simple tests, such as the sign and runs tests, and the Kruskal-Wallis, Friedman and Durbin tests, tests of the most frequently encountered experimental designs, the completely randomised, randomised block and balanced incomplete block design respectively. Permutation testing, a fundamental nonparametric tool in its own rite, is introduced to calculate p-values for the tests discussed. A companion text gives detail of R code used throughout Introductory Nonparametrics.</p>Tue, 09 Jan 2018 20:03:47 GMTJ.C.W. Rayner University of Newcastle, AustraliaA Handbook of Statistics An Overview of Statistical Methods
https://www.merlot.org/merlot/viewMaterial.htm?id=1351952
<p>A Handbook for Statistics provides readers with an overview of common statistical methods used in a wide variety of disciplines. The book focuses on giving the intuition behind the methods as well as how to execute methods using Microsoft Excel. Handbook for Statistics is divided into five main sections. The first section discusses why we study statistics and how we apply statistics to solve problems. The second section covers descriptive statistics which covers different ways to describe large data sets. Section three covers probability and probability distributions. Section four gives an overview of inference. Finally section five covers correlation and simple linear regression.</p>Wed, 13 Dec 2017 02:45:08 GMTDarius SingpurwallaIntroductory Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1350435
<p>Introductory Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a Try It problem that is designed as extra practice for students. This book also includes collaborative exercises and statistics labs designed to give students the opportunity to work together and explore key concepts. While the book has been built so that each chapter builds on the previous, it can be rearranged to accommodate any instructor’s particular needs.</p>
<p>This book is used by Lethbridge College and was derived from Lyryx Learning.</p>Thu, 07 Dec 2017 17:48:19 GMTBusiness Statistics I
https://www.merlot.org/merlot/viewMaterial.htm?id=1350433
<p id="eip-296">You are probably asking yourself the question, "When and where will I use statistics?" If you read any newspaper, watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a television news program, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical methods can help you make the "best educated guess."</p>
<p id="eip-1000">Since you will undoubtedly be given statistical information at some point in your life, you need to know some techniques for analyzing the information thoughtfully. Think about buying a house or managing a budget. Think about your chosen profession. The fields of economics, business, psychology, education, biology, law, computer science, police science, and early childhood development require at least one course in statistics.</p>
<p>This book is used by Mt Royal University and was derived from OpenStax Business Statistics and Lyryx Learning.</p>Thu, 07 Dec 2017 17:44:11 GMTLyryx LearningApplied Probability
https://www.merlot.org/merlot/viewMaterial.htm?id=1330147
<p>Although classified as a "course" this could be used as an open textbook for the COOL4Ed project.</p>
<p id="id84958">This is a "first course" in the sense that it presumes no previous course in probability. The units are modules taken from the unpublished text: Paul E. Pfeiffer, ELEMENTS OF APPLIED PROBABILITY, USING MATLAB. The units are numbered as they appear in the text, although of course they may be used in any desired order. For those who wish to use the order of the text, an outline is provided, with indication of which modules contain the material.</p>
<p id="id84965">The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. <a href="https://cnx.org/contents/1cb4ffaa-f24a-4b98-8e03-5dd217011f0c@6.2:95d19203-436b-4291-887b-974cb4064982@6" data-page="52">Appendix B</a> provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.</p>
<p id="id84973">In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as <em data-effect="italics">m-programs</em>, or simply <em data-effect="italics">programs</em>) to solve many important problems in basic probability. This should make the work useful as a stand alone exposition as well as a supplement to any of several current textbooks.</p>
<p id="id84990">Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals).</p>
<p id="id84997">Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called <em data-effect="italics">minterm analysis</em>. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.</p>Tue, 12 Sep 2017 21:16:32 GMTPaul E. Pfeiffer Rice UniversityApplied Probability
https://www.merlot.org/merlot/viewMaterial.htm?id=1330145
<p>Although classified as a "course" this could be used as an open textbook for the COOL4Ed project.</p>
<p id="id84958">This is a "first course" in the sense that it presumes no previous course in probability. The units are modules taken from the unpublished text: Paul E. Pfeiffer, ELEMENTS OF APPLIED PROBABILITY, USING MATLAB. The units are numbered as they appear in the text, although of course they may be used in any desired order. For those who wish to use the order of the text, an outline is provided, with indication of which modules contain the material.</p>
<p id="id84965">The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. <a href="https://cnx.org/contents/1cb4ffaa-f24a-4b98-8e03-5dd217011f0c@6.2:95d19203-436b-4291-887b-974cb4064982@6" data-page="52">Appendix B</a> provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.</p>
<p id="id84973">In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as <em data-effect="italics">m-programs</em>, or simply <em data-effect="italics">programs</em>) to solve many important problems in basic probability. This should make the work useful as a stand alone exposition as well as a supplement to any of several current textbooks.</p>
<p id="id84990">Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals).</p>
<p id="id84997">Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called <em data-effect="italics">minterm analysis</em>. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.</p>Tue, 12 Sep 2017 21:16:03 GMTPaul E. Pfeiffer Rice UniversityStatistical Inference For Everyone
https://www.merlot.org/merlot/viewMaterial.htm?id=1328192
<p>This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.</p>Wed, 06 Sep 2017 01:32:12 GMTBrian Blais Bryant UniversityPrinciples of Business Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1328110
<p>This textbook contains the following chapters:</p>
<p style="padding-left: 30px;">1 Sampling and Data<br /> 2 Descriptive Statistics<br /> 3 The Normal Distribution<br /> 4 Confidence Interval<br /> 5 Hypothesis Testing<br /> 6 Linear Regression and Correlation</p>Tue, 05 Sep 2017 19:23:14 GMTMultiple authors OpenStaxIntroduction to Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1325331
<p><em>Introduction to Statistics</em> is a resource for learning and teaching introductory statistics.</p>
<p id="Table-contents">Table of Contents: </p>
<p>1. Introduction<br />2. Graphing Distributions<br />3. Summarizing Distributions<br />4. Describing Bivariate Data<br />5. Probability<br />6. Research Design<br />7. Normal Distributions<br />8. Advanced Graphs<br />9. Sampling Distributions <br />10. Estimation <br />11. Logic of Hypothesis Testing <br />12. Testing Means<br />13. Power<br />14. Regression <br />15. Analysis of Variance<br />16. Transformations<br />17. Chi Square <br />18. Distribution-Free Tests<br />19. Effect Size<br />20. Case Studies<br />21. Glossary</p>Thu, 24 Aug 2017 20:12:02 GMTDavid Lane Rice UniversityConcepts in Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1323021
<p>We organized this course around the Big Picture of Statistics. As we learn new material, we will always look at how these new ideas relate to the Big Picture. In this way the Big Picture is a diagram that will help us organize and understand the material we will learn throughout the course.</p>
<p>The Big Picture summarizes the steps in a statistical investigation.</p>
<p>This is a course that is offered by Lumen Learning. Although a course, it can easily be used as a textbook and has multiple links to OERs, including readings and videos. Lumen Learning’s mission is to make great learning opportunities available to all students, regardless of socioeconomic background.</p>
<p><strong>This is being categorized as an interactive textbook for the COOL4Ed project.</strong></p>
<p>This course is taught using a mastery approach. It was designed to give you the best opportunity for success. Your instructor will guide you through the process, but below are some important things to keep in mind as you begin.</p>
<p>Course Structure</p>
<p style="padding-left: 30px;">• Each course is built around Competencies, which are important skills or knowledge that can be used in the real world</p>
<p style="padding-left: 30px;">• Each Competency has enabling Learning Outcomes that teach you what you need to know to master the Competency</p>
<p style="padding-left: 30px;">• Each Learning Outcome is supported by Open Educational Resources, which are a range of materials that will help you build your skills and knowledge of the learning outcomes.</p>Wed, 16 Aug 2017 16:15:03 GMTThe Philosophy and Science of Multivariate Reasoning
https://www.merlot.org/merlot/viewMaterial.htm?id=1268805
<p>This book has been specifically written and edited to support classes dealing with the subjects of critical thinking and reasoning, introduction to scientific methods, or basic research methods. A variety of examples and references have been incorporated specifically to support students pursuing degrees in the sciences, social sciences, and liberal arts. The writing style and grammar used in the book have been purposely tailored to support the needs of undergraduate education, but can also be valuable in support of a graduate level review course. </p>Sun, 22 Jan 2017 16:37:09 GMTHarold CampbellUnderstanding Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1246011
<p>This is a free textbook offered by BookBoon.</p>
<p>This is a book on the understanding of statistical concepts. If you have no knowledge, you will receive basic knowledge, without having to worry much about mathematics. And if you already know something about statistical methods, you will get a better understanding of the ideas behind them. All basic concepts are discussed in detail and illustrated with examples. Learn more about error margins, analysis of variance, significance, confidence intervals, bootstrap, regression, analysis of scale data, variance components and other concepts. All simply explained and illustrated with examples.</p>Thu, 03 Nov 2016 17:39:41 GMTSture HolmSaylor Academy Open Textbooks
https://www.merlot.org/merlot/viewMaterial.htm?id=1227848
<p>Saylor Academy, one of the leaders in Open Education, offers educators, students, and families 100+ free open textbooks published under various Creative Commons license agreements. </p>Sat, 03 Sep 2016 20:08:25 GMTSaylor Academy Staff Saylor Academy, USAIntroductory Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1221239
<p>In many introductory level courses today, teachers are challenged with the task of fitting in all of the core concepts of the course in a limited period of time. The Introductory Statistics teacher is no stranger to this challenge. To add to the difficulty, many textbooks contain an overabundance of material, which not only results in the need for further streamlining, but also in intimidated students. Shafer and Zhang wrote Introductory Statistics by using their vast teaching experience to present a complete look at introductory statistics topics while keeping in mind a realistic expectation with respect to course duration and students’ maturity level.</p>
<p>Over time the core content of this course has developed into a well-defined body of material that is substantial for a one-semester course. Shafer and Zhang believe that the students in this course are best served by a focus on that core material and not by an exposure to a plethora of peripheral topics. Therefore in writing Introduction to Statistics they have sought to present only the core concepts and use a wide-ranging set of exercises for each concept to drive comprehension. As a result Introduction to Statistics is a smaller and less intimidating textbook that trades some extended and unnecessary topics for a better-focused presentation of the central material.</p>
<p>You will not only appreciate the depth and breadth of exercises in Introduction to Statistics, but you will also like the close attention to detail that Shafer and Zhang have paid to the student and instructor solutions manuals. This is one of few books on the market where the textbook authors have written the solutions manuals to maintain the integrity of the material.</p>
<p>In addition, in order to facilitate the use of technology with the book the authors included “large data set exercises,” where appropriate, that refer to large data sets that are available on the web, and for which use of statistical software is necessary.</p>Fri, 12 Aug 2016 01:39:29 GMTApplied Probability
https://www.merlot.org/merlot/viewMaterial.htm?id=1221176
<p>This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.</p>
<p>In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks.</p>
<p>Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals).</p>
<p>Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.</p>Thu, 11 Aug 2016 21:56:56 GMTPaul E. Pfeiffer Rice UniversityThink Stats: Probability and Statistics for Programmers
https://www.merlot.org/merlot/viewMaterial.htm?id=1215030
<p><em>Think Stats</em> is an introduction to Probability and Statistics for Python programmers.</p>
<ul><li><em>Think Stats </em>emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.</li><li>If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.</li></ul>Wed, 03 Aug 2016 21:57:16 GMTAllen B. Downey Olin CollegeLectures on Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1211338
<p>'These notes are based on a course that I gave at UIUC in 1996 and again in 1997. No prior knowledge of statistics is assumed. A standard first course in probability is a prerequisite, but the first 8 lectures review results frombasic probability that are important in statistics. Some exposure to matrix algebra is needed to cope with the multivariate normal distribution in Lecture 21, and there is a linear algebra review in Lecture 19.'</p>Mon, 01 Aug 2016 21:26:32 GMTRobert B. Ash UIUCIntroduction to Statistical Thought
https://www.merlot.org/merlot/viewMaterial.htm?id=1211331
<p>The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. "Statistical thinking" means a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. "Likelihood emphasis" means that the likelihood function and likelihood principle are unifying ideas throughout the text.</p>
<p>Another unusual aspect is the use of statistical software as a pedagogical tool. That is, instead of viewing the computer merely as a convenient and accurate calculating device, the book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts. The book is written with the statistical language <strong>R</strong> embedded throughout. <strong>R</strong> and accompanying manuals are available for free download.</p>Mon, 01 Aug 2016 21:21:36 GMTMichael Lavine