An Introduction to Statistics

Short Description:
Significant Statistics: An Introduction to Statistics was adapted and original content added by John Morgan Russell. It is adapted from content published by OpenStax Introductory Statistics, OpenIntro Statistics, and Introductory Statistics for the Life and Biomedical Sciences. NewParaNote to instructors: This book is undergoing active peer review and copyediting. It may change. Please complete this form https://bit.ly/stat-interest to be notified of the status of the book.NewParaSignificant Statistics: An Introduction to 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 'Your Turn' problem that is designed as extra practice for students. NewParaInstructors reviewing, adopting, or adapting this textbook, please help us understand your use by filling out this form: https://bit.ly/stat-interest.

Long Description:
Significant Statistics: An Introduction to 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 ‘Your Turn’ problem that is designed as extra practice for students.

Word Count: 275456

ISBN: 978-1-949373-37-0

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

An important objective in hiring is to ensure diversity in the workforce. The race or gender of individuals hired by an organization should reflect the race or gender of the applicant pool. If certain groups are under-represented or over-represented among the employees, then there may be a case for discrimination in hiring. On the other hand, there may be a number of random factors unrelated to discrimination, such as the timing of the interview or competition from other employers, that might cause one group to be over-represented or under-represented. In this exercise, we ask students to investigate the role of randomness in hiring, and to consider how this might be used to help substantiate or refute charges of discrimination.

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.
This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

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.

This resource consists of a Java applet and expository text. The applet simulates the order statistics of a random sample from a given distribution. The sample size, order, and sampling distribution can be specified.

This course discusses the principles and methods of statistical mechanics. Topics covered include classical and quantum statistics, grand ensembles, fluctuations, molecular distribution functions, other concepts in equilibrium statistical mechanics, and topics in thermodynamics and statistical mechanics of irreversible processes.

Topics List for this Lesson: Fundamental Counting PrincipleFundamentals of ProbabilityNOT and OR EventsAND Events

This site teaches High Schoolers Modeling with Geometry through a series of 1548 questions and interactive activities aligned to 12 Common Core mathematics skills.

This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32 Econometrics. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing.

A general statistics course, which includes understanding data, measures of central tendency, measures of variation, binomial distributions, normal distributions, correlation and regression, probability and sampling distributions, Central Limit Theorem, confidence intervals, estimates of population parameters and hypothesis testing. Interpretation and data analysis are emphasized.

This is a course on the fundamentals of probability geared towards first or second-year graduate students who are interested in a rigorous development of the subject. The course covers sample space, random variables, expectations, transforms, Bernoulli and Poisson processes, finite Markov chains, and limit theorems. There is also a number of additional topics such as: language, terminology, and key results from measure theory; interchange of limits and expectations; multivariate Gaussian distributions; and deeper understanding of conditional distributions and expectations.

Students will explore the overarching question, is the internet trustworthy?, while expanding their knowledge of statistics and comparing data sets.  They will compare two data sets as well as determine the accuracy or bias of data representations shown on the internet. Students will then have the opportunity to apply their knowledge while creating their own visual representations for data they personally collected regarding the trustworthiness of the internet.  The module concludes with a peer showcase and the post-assessment.

This resource is geared for teacher use. It is loosely linked to the Secondary Math II, Mathematics Vision Project curriculum. 

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.

Here is a lesson that will challenge students ot make predication based on expected results.  You will need two dice for each pair of students in your classroom.  A token of some sort will help students mark their progress in the game.  The Dice Game is a pair activity, and the Dice Assessment is a stand alone activity where teachers can check student understanding.Activity 1:  In pairs, have students roll two dice ten times each.  Have the other student record the results.  What numbers show up the most often?Activity 2: Dice Game.  Have the students follow the sheet and fill it out togetherActivity 3: Dice Assessment.  Have students individually complete the assessment.

This supplemental resource provides problems and activities related to Probability and Discrete Mathematics in Middle School.

The Course includes the concept of probability distribution mainly binomial, poisson and normal distribution. In this course students will understood how to solve probability distribution problems using definition and open source software's like GeoGebra and excel