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• Statistics and Probability
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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

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
OpenIntro
Author:
Christopher Barr
David Diez
Leah Dorazio
Mine Cetinkaya-Rundel
04/27/2020
Unrestricted Use
CC BY
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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 The units are modules taken from the unpublished text: Paul E. Pfeiffer, ELEMENTS OF APPLIED PROBABILITY, USING MATLAB.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.

Subject:
Statistics and Probability
Material Type:
Full Course
Provider:
Rice University
Provider Set:
OpenStax CNX
Author:
Paul E. Pfeiffer
09/18/2009
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Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018
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Describe a bivariate relationship's linearity, strength, and direction. In other words, plotting things that take two variables into consideration and trying to see whether there's a pattern with how they relate.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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This is a brief conditional probability examplediscussing probabilities like P(A | B) using breakfast and lunch as examples.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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This video talka about what is easily one of the most fundamental and profound concepts in statistics and maybe in all of mathematics. And that's the central limit theorem.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
Unrestricted Use
CC BY
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Published by OpenStax College, Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two and fouryear colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
Rice University
Provider Set:
OpenStax CNX
Author:
Barbara Ilowsky
Susan Dean
07/09/2014
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Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise).

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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This video defines the differences between discrete and continuous random variables, then works through examples of each.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
02/28/2018
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1). Summarize and describe the distribution of a categorical variable in context.
2). Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot).
3). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern.
4). Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts.
5). Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context.
5). Apply the standard deviation rule to the special case of distributions having the "normal" shape.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018
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Compare distributions using the features of shape, center, spread, and outliers.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
02/28/2018
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A set of step by step walk through of a hypothesis testing

one proportion z-test using p-values
Chi-square goodness of fit using p-values
one mean with population standard deviation is known using p-values
one mean with population standard deviation is unknown, using p-value

Subject:
Statistics and Probability
Material Type:
Module
Author:
Laura Ralston
03/02/2018
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Short video which describes the difference between a sample and a population.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
Montana State University
Author:
Mark C. Greenwood
10/29/2021
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Sal shows examples of intersection and union of sets and introduces some set notation.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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Introduction to Financial Mathematics: Concepts and Computational Methods serves as a primer in financial mathematics with a focus on conceptual understanding of models and problem solving. It includes the mathematical background needed for risk management, such as probability theory, optimization, and the like. The goal of the book is to expose the reader to a wide range of basic problems, some of which emphasize analytic ability, some requiring programming techniques and others focusing on statistical data analysis. In addition, it covers some areas which are outside the scope of mainstream financial mathematics textbooks. For example, it presents marginal account setting by the CCP and systemic risk, and a brief overview of the model risk. Inline exercises and examples are included to help students prepare for exams on this book.

Subject:
Finance
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
Florida State University
Provider Set:
DigiNole
Author:
Arash Fahim
07/08/2019
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We are constantly bombarded by information, and finding a way to filter that information in an objective way is crucial to surviving this onslaught with your sanity intact. This is what statistics, and logic we use in it, enables us to do. Through the lens of statistics, we learn to find the signal hidden in the noise when it is there and to know when an apparent trend or pattern is really just randomness. The study of statistics involves math and relies upon calculations of numbers. But it also relies heavily on how the numbers are chosen and how the statistics are interpreted.

This work was created as part of the University of Missouri’s Affordable and Open Access Educational Resources Initiative (https://www.umsystem.edu/ums/aa/oer). The contents of this work have been adapted from the following Open Access Resources: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Changes to the original works were made by Dr. Garett C. Foster in the Department of Psychological Sciences to tailor the text to fit the needs of the introductory statistics course for psychology majors at the University of Missouri – St. Louis. Materials from the original sources have been combined, reorganized, and added to by the current author, and any conceptual, mathematical, or typographical errors are the responsibility of the current author.

Subject:
Statistics and Probability
Psychology
Material Type:
Textbook
Author:
Dan Osherson
Foster Garett C
Garett C Foster
Hebl Mikki
Mikki Hebl
Rice University
Rudy Guerra
Scott David
University Of Missouri-st Louis
Zimmer Heidi
04/27/2020
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The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
Benjamin Yakir
10/31/2021
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Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
David Lane
04/27/2020
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The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Thomas K. Tiemann
04/27/2020
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CC BY
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
Rice University
Provider Set:
Openstax College
Author:
Alexander Holmes
Barbara Illowsky
Susan Dean
11/30/2017
Unrestricted Use
CC BY
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"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Mohammad Mahbobi, Thompson Rivers University; Thomas K. Tiemann, Elon University
04/19/2016
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
The Saylor Foundation
Author:
Douglas S. Shafer
Zhiyi Zhang
11/09/2021
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
Rice University
Provider Set:
Openstax College
Author:
Barbara Ilowsky
Susan Dean
07/19/2013
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
OpenIntro
Author:
Christopher Barr
David Diez
Mine Çetinkaya-Runde
04/27/2020
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This is a first draft of a free (as in speech, not as in beer, [Sta02]) (although it is free as in beer as well) textbook for a one-semester, undergraduate statistics course. It was used for Math 156 at Colorado State University–Pueblo in the spring semester of 2017.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
Jonathan A. Poritz
04/27/2020
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This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
Rachel L. Webb
10/27/2021
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Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
State University of New York
Provider Set:
Milne Open Textbooks
Author:
Diane Kiernan
01/16/2014
Only Sharing Permitted
CC BY-NC-ND
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
OpenIntro
Author:
Christopher Barr
David Diez
Mine Cetinkaya-Rundel
01/01/2011
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Welcome to behavioral statistics, a statistics textbook for social science majors!

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
LibreTexts
10/31/2021
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LEARNING OBJECTIVE: Identify and distinguish between a parameter and a statistic.

LEARNING OBJECTIVE: Explain the concepts of sampling variability and sampling distribution.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018
Unrestricted Use
CC BY
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You are probably asking yourself the question, "When and where will I use statistics?". If you read any newspaper or 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 news program on television, 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."

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
Rice University
Provider Set:
OpenStax CNX
Author:
Mihai Nica
04/27/2020
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Examples finding probabilities from probability distributions for continuous random variables.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018
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1). Identify the sampling method used in a study and discuss its implications and potential limitations.
2). Critically evaluate the reliability and validity of results published in mainstream media.
3). Summarize and describe the distribution of a categorical variable in context.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018
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This activity is an advanced version of the “Keep your eyes on the ball” activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Provider Set:
Causeweb.org
Author:
Trent D. Buskirk and Linda J. Young, University of Nebraska-Lincoln and University of Nebraska-Lincoln
02/28/2018
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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.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
Brian Blais
04/27/2020
Unrestricted Use
CC BY
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A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item. Age, sex, business income and expenses, country of birth, capital expenditure, class grades, eye colour and vehicle type are examples of variables. It is called a variable because the value may vary between data units in a population, and may change in value over time. There are different ways variables can be described according to the ways they can be studied, measured, and presented.

Subject:
Statistics and Probability
Material Type:
Diagram/Illustration
Provider:
Australian Bureau of Statistics