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Statistics Using Technology
Kathryn Kozak · Coconino Community College
Có trong mục lục: 1.01: What is Statistics · 1.04: How Not to Do Statistics · 03: Examining the Evidence Using Graphs and Statistics
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Inferential Statistics and Probability - A Holistic Approach
Maurice A. Geraghty · De Anza College
Có trong mục lục: 03: Descriptive Statistics · 5.10: Changing the Conditionality and Bayesian Statistics · 9.01: Inferential Statistics
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Intermediate Statistics with R
Mark Greenwood · Montana State University
Có trong mục lục: 1.03: Basic summary statistics histograms and boxplots using R · 02: (R)e-Introduction to statistics
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Mostly Harmless Statistics
Rachel L. Webb · Portland State University
Có trong mục lục: 03: Descriptive Statistics
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Foundations in Statistical Reasoning
Peter Kaslik · Pierce College Fort Steilacoom
Có trong mục lục: 03: Examining the Evidence using Graphs and Statistics · 3.E: Examining the Evidence using Graphs and Statistics (Exercises)
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Introductory Probability
Charles Grinstead & J. Laurie Snell · Dartmouth & Swarthmore Universities
This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject. The text can be used in a variety of course lengths, levels, and areas of emphasis.
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Support Course for Elementary Statistics
Larry Green · Lake Tahoe Community College
This course covers topics that support a transfer-level Statistics course. It covers only developmental topics but includes notation or examples that are relevant to a Statistics course. It could also be used as a prerequisite course.
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Learning Statistics with R - A tutorial for Psychology Students and other Beginners
DJ Navarro · University of New South Wales
Có trong mục lục: 01: Why Do We Learn Statistics · 1.01: On the Psychology of Statistics · 1.03: Statistics in Psychology
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Visual Statistics Use R!
Alexey Shipunov · Minot State University
The ignorance of the statistical methods can lead to mistakes and misinterpretations. Unfortunately, understanding of these methods is far from common. Many college majors require a course in probability theory and mathematical statistics, but all many of us remember from these courses is horror and/or frustration at complex mathematical formulas filled with Greek letters, some of them wearing hats.
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Statistics C1000: Introduction to Statistics
Toros Berberyan, Tracy Nguyen, and Alfie Swan · Citrus College
Có trong mục lục: 01: Fundamentals of Statistics · 1.01: What is Statistics · 1.04: How Not to Do Statistics
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Analytical Chemistry 2.1
David Harvey · DePauw University
The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum for exploring topics such as experimental design, sampling, calibration strategies, standardization, optimization, statistics, and the validation of experimental results. Analytical methods come and go, but best practices for designing and validating analytical methods are universal.
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Statistics Done Wrong
Alex Reinhart · Carnegie Mellon University
Có trong mục lục: 02: Statistical Power and Underpowered Statistics
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A Contemporary Approach to Research and Statistics in Psychology
Klaire Somoray · James Cook University
Có trong mục lục: 1.01: What can Statistics do for us · 1.03: Big Ideas in Statistics · 09: Beyond Research and Statistics
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Statistics Through an Equity Lens (Anthony)
Yvonne Anthony · Massachusetts Bay Community College
Có trong mục lục: 1.01: An Overview of Statistics · 1.02: Descriptive Statistics · 1.04: Inferential Statistics- Sampling Methods
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Statistical Thinking for the 21st Century
Russell A. Poldrack · Stanford University
Có trong mục lục: 1.02: Dealing with Statistics Anxiety · 1.03: What Can Statistics Do for Us · 1.04: The Big Ideas of Statistics
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Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R)
Hank Jenkins-Smith; Joseph Ripberger · University of Oklahoma Libraries · 2017
The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models.The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.
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Analytical Chemistry 2.1
David Harvey · David T. Harvey · 2016
As currently taught in the United States, introductory courses in analytical chemistry emphasize quantitative (and sometimes qualitative) methods of analysis along with a heavy dose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection of analytical methods and an understanding of equilibrium chemistry; it is an approach to solving chemical problems. Although equilibrium chemistry and analytical methods are important, their coverage should not come at the expense of other equally important topics. The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum for exploring topics such as experimental design, sampling, calibration strategies, standardization, optimization, statistics, and the validation of experimental results. Analytical methods come and go, but best practices for designing and validating analytical methods are universal. Because chemistry is an experimental science it is essential that all chemistry students understand the importance of making good measurements. My goal in preparing this textbook is to find a more appropriate balance between theory and practice, between “classical” and “modern” analytical methods, between analyzing samples and collecting samples and preparing them for analysis, and between analytical methods and data analysis. There is more material here than anyone can cover in one semester; it is my hope that the diversity of topics will meet the needs of different instructors, while, perhaps, suggesting some new topics to cover.
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A Contemporary Approach to Research and Statistics in Psychology
Klaire Somoray · Council of Australian University Librarians · 2023
This book is designed as a short and introductory resource specifically aimed to help students grasp key concepts typically found in undergraduate psychology statistics subjects. It was initially developed for online and intensive programs, which are becoming increasingly popular in psychology education. The book utilises jamovi, a free and open-source statistical software. This peer reviewed book is completely free to download, use, and adapt and it is released using the CC BY-NC 4.0 licence.
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Significant Statistics
John Russell · Virginia Tech Publishing · 2025
Significant Statistics: An Introduction to Statistics is intended for students enrolled in a one-semester introduction to statistics course 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 'Your Turn' problem that is designed as extra practice for students. Significant Statistics: An Introduction to Statistics was adapted from content published by OpenStax including Introductory Statistics, OpenIntro Statistics, and Introductory Statistics for the Life and Biomedical Sciences. John Morgan Russell reorganized the existing content and added new content where necessary. Instructors reviewing, adopting, or adapting this textbook: please help us understand your use by filling out this form: https://bit.ly/stat-interest.
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Introduction to Probability
John Baxter · John R. Baxter · 2026
This is an introduction to probability theory, designed for self-study. It covers the same topics as the one-semester introductory courses which I taught at the University of Minnesota, with some extra discussion for reading on your own. The reasons which underlie the rules of probability are emphasized. Probability theory is certainly useful. But how does it feel to study it? Well, like other areas of mathematics, probability theory contains elegant concepts, and it gives you a chance to exercise your ingenuity, which is often fun. But in addition, randomness and probability are part of our experience in the real world, present everywhere and yet still somewhat mysterious. This gives the subject of probability a special interest.