PowerPoint Slides to accompany Chapter 3 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 4 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 6 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 7 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 8 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 9 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

Passion-Driven Statistics is an NSF-funded, multidisciplinary, project-based curriculum that supports students in conducting data-driven research, asking original questions, and communicating methods and results using the language of statistics. The curriculum supports students to work with existing data covering psychology, health, earth science, government, business, education, biology, ecology and more. From existing data, students are able to pose questions of personal interest and then use statistical software (e.g. SAS, R, Python, Stata, SPSS) to answer them. The e-book is presented in pdf format for ease of use across platforms.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

The students will play a classic game from a popular show. Through this they will see the probabilty that the ball will land each of the numbers with more accurate results coming from repeated testing.

This course introduces students to the conduct of political research using quantitative methodologies. The methods are examined in the context of specific political research activities like public opinion surveys, voting behavior, Congressional behavior, comparisons of political processes in different countries, and the evaluation of public policies. Students participate in joint class projects and conduct individual projects.

These practice tests from MIT OpenCourseWare cover topics in the Introductory Probability and Statistics course. They were originally written by Professors Jeremy Orloff and Jonathan Bloom, and adapted by the team at OpenStax CreatorFest. The files include both assessments and solutions.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

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."

The addition rule for probability is explained using Venn Diagrams. [Probability playlist: Lesson 3 of 29]

CK-12 Foundation's new and improved Advanced Probability and Statistics-Second Edition FlexBook introduces students to basic topics in statistics and probability, but finishes with the rigorous topics an advanced placement course requires.

CK-12 Advanced Probability and Statistics introduces students to basic topics in statistics and probability but finishes with the rigorous topics an advanced placement course requires. Includes visualizations of data, introduction to probability, discrete probability distribution, normal distribution, planning and conducting a study, sampling distributions, hypothesis testing, regression and correlation, Chi-Square, analysis of variance, and non-parametric statistics.

CK-12 Advanced Probability and Statistics Teacher's Edition provides tips and enrichment activities for teaching CK-12 Advanced Probability and Statistics Student Edition. The solution and assessment guides are available upon request.

Probability & Statistics introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and inferential methods. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. The course does not assume any prior knowledge in statistics and its only prerequisite is basic algebra.

To access this course, click "Enter Open & Free Course," then "Enter course" under "Enter without an account."