Digital library
Textbooks, references and open learning resources.
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Polimetrics: A Stata Companion to Introduction to Political Science Research Methods
Josh Franco · Open Political Science (OPoliSci) · 2021
Polimetrics: A Stata Companion, authored by Dr. Josh Franco, is an Open Education Resource workbook licensed CC BY-NC and designed as a Stata companion to Introduction to Political Science Research Methods. This workbook provides a tour of the Stata software, an introduction to cross-sectional, time series, and panel data, and an introduction to a variety of models. I review models where the outcome is linear, binary, ordinal, categorical, and count. Additionally, I have an interpretation chapter on survival models. Each “Models” chapter has a similar organizational structure: about, estimated time, what is the model, how are models run in Stata, how do we interpret the model results, and a real-world example of model results in a Creative Commons licensed, peer-reviewed journal article. Additionally, mini-assignment instructions and a rubric are included so students can practice their interpretation skills.
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Contemporary Mathematics
Donna Kirk · OpenStax · 2023
Contemporary Mathematics is designed to meet the scope and sequence requirements for a liberal arts mathematics course. This resource provides stand-alone sections with a focus on showing relevance in the features as well as the examples, exercises, and exposition. Contemporary Mathematics integrates technology applications, projects, and highlights a diverse group of contributors to mathematics, statistics, and related fields. Print copies of this book will be available for the fall 2023 semester. You can sign up to be notified when print copies are available.
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Statistical Inference For Everyone
Brian Blais · Brian Blais · 2017
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.
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Applied Probability
Paul Pfeiffer · OpenStax CNX · 2009
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. 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. 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). 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.
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Chemical Process Dynamics and Controls
Peter Woolf · openmichigan · 2009
Process controls is a mixture between the statistics and engineering discipline that deals with the mechanism, architectures, and algorithms for controlling a process. Some examples of controlled processes are: •Controlling the temperature of a water stream by controlling the amount of steam added to the shell of a heat exchanger. •Operating a jacketed reactor isothermally by controlling the mixture of cold water and steam that flows through the jacket of a jacketed reactor. •Maintaining a set ratio of reactants to be added to a reactor by controlling their flow rates. •Controlling the height of fluid in a tank to ensure that it does not overflow.
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Contemporary Mathematics
Donna Kirk; Barbara Boschmans; Brian Beaudrie; Matthew Cathey · OpenStax, Rice University
Contemporary Mathematics is designed to meet the scope and sequence requirements for a liberal arts mathematics course. This resource provides stand-alone sections with a focus on showing relevance in the features as well as the examples, exercises, and exposition. Contemporary Mathematics integrates technology applications, projects, and highlights a diverse group of contributors to mathematics, statistics, and related fields.
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Free Energy 1e (Snee)
Preston Snee · University of Illinois Chicago
In the contents: 11: Boltzmann Statistics
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Math 150: Survey of Math
Leah Griffith, Veronica Holbrook, Johnny Johnson & Nancy Garcia · Rio Hondo College
In the contents: 05: Statistics · 5.01: What is Statistics
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Math In Plain Sight
Timothy Cooley · Orange Coast College
In the contents: 06: Statistics
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Quantway Core
Carnegie Math Pathways · WestEd
In the contents: 2.07: Making Good Decisions with Good Statistics
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The Science of Psychology
Michael Miguel · Coalinga College
In the contents: 10: Descriptive Statistics · 11: Inferential Statistics · 11.01: Prelude to Inferential Statistics
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College Mathematics - Abridged Edition
Cayla M Gaworecki · SUNY Schenectady County Community College
In the contents: 08: Statistics
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Thermodynamics and Statistical Mechanics
Daniel Arovas · UC San Diego
In the contents: 5.05: Photon Statistics
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Statistical Applications in Psychological Sciences with Multimedia
Chanler Hilley · Kennesaw State University
In the contents: 1.01: What are statistics and why do we study them
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ENVS C100: Environmental Science
Tanya Hoerer · Coastline College
In the contents: 1.3.02: Summarizing the data- Descriptive statistics · 1.3.03: Putting it all together- Inferential statistics and hypothesis testing
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Microbiology Fundamentals Lab Manual
Heather Deter · Concordia University Chicago
In the contents: 2.03: Reading- Descriptive Statistics and Graphing
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Coastal Dynamics
Judith Bosboom & Marcel J.F. Stive · Delft University of Technology
In the contents: 3.04: Short-term wave statistics · 3.06: Long term statistics and extreme values
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Precalculus
CK12 · CK12
In the contents: 15: Concepts of Statistics
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General Ecology
Tara Jo Holmberg · CT State Northwestern
In the contents: 1.5: Summarizing Data - Descriptive Statistics · 1.6: Testing Hypotheses - Inferential Statistics
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Calculus
Gilbert Strang & Edwin Herman · MIT & University of Wisconsin-Stevens Point
The text guides students through the core concepts of calculus and helps them understand how those concepts apply to their lives and the world around them.