# Resource Library

#### Statistical Topic

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• ### A First Lesson in Bayesian Inference (uses Shiny Apps)

This page supports an in-class exercise that highlights several key Bayesian concepts. The scenario is as follows: a large paper bag contains pieces of candy with wrappings of different color, and we are interested in learning about the unknown proportion of yellow-wrapped pieces of candy. After completing the exercises, we will be familiar with the following concepts and ideas: probability distributions can quantify degree of beliefprior distributionposterior distributionsequential updatingconjugacy, Cromwell’s Rule (http://en.wikipedia.org/wiki/Cromwell's_rule), the data overwhelm the prior, Bayes factors, Savage-Dickey density ratio, sensitivity analysiscoherence.

• ### R Tutorials in Data Science

This page presents a series of tutorials and interdisciplinary case studies that can be used in a variety of blended as well as brick-and-mortar courses. The materials can be used in introductory level data science courses as well as more advanced data science or statistics courses.  These materials assume that students have a basic prior knowledge of R or Rstudio.

• ### Practicing Statistics: Guided Investigations for the Second Course

The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

• An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
• guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
• Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
• Data sets from multiple disciplines and software instructions for Minitab and R.

The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class.

• ### African Lions: Modeling Populations

Learn to distinguish between exponential and logistic growth of populations, identify carrying capacity, differentiate density-dependent and density-independent limiting factors, apply population models to data sets and determine carrying capacity from population data. Make predictions on graphs and interpret graphical data to analyze factors that influence population growth.

This link includes a lesson plan, assessment materials, and access to SmartGraphs, a software that helps students create and interpret graphs.

• ### Chance Teaching Aids

This is a site that contains a number of types of material that can be used in teaching about chance and probability. Lesson plans, syllabi, suggested activities, and data sets are available. The data sets contain interesting information for students such as: quarterback passing rating data, baseball streaks, and baseball salaries that can be used to illustrate means, medians, etc.. The site also contains a link to the Chance News (which is now a wiki on CAUSEweb).

• ### Measures of Central Tendency and Outliers PowerPoint

Share with your students why the presence of an outlier affects which measure of central tendency to report. Feel free to modify this Powerpoint presentation to fit the needs of your students. Included at the end are additional online resources to further engage your students in their learning about the mean, median, and mode. The presentation is covered by a Creative Commons Attribution-Share Alike 3.0 License.

• ### STATS Issue 49 Winter 2009

This issue contains articles about Karl Pearson (150 years after his birth); finding more ways to make learning statistics fun; simulating capture-recapture sampling in Excel and by hand; common misconceptions in statistics; a correlation-based puzzler and a STAT.DOKU puzzle.

• ### Stat.Istics site

This site is a collection of interesting stories in the news that relate to statistics, major league baseball standings, links to textbooks, and links to applets. It also contains some reflections on statistical issues from retired professor John Marden (from University of Illiois at Urbana-Champaign).

• ### Lesson Plan: Univariate Statistics

This is a lesson plan for 16 to 17 year old students that focuses on developing students' understanding of the relative strengths and weaknesses of various representations of real world univariate statistics. Students work in groups to research different visual representations and create a wiki page of their findings.

• ### STatistics Education Web (STEW)

Statistics and probability concepts are included in K–12 curriculum standards—particularly the Common Core State Standards—and on state and national exams. STEW provides free peer-reviewed teaching materials in a standard format for K–12 math and science teachers who teach statistics concepts in their classrooms.

STEW lesson plans identify both the statistical concepts being developed and the age range appropriate for their use. The statistical concepts follow the recommendations of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K-12 Curriculum Framework, Common Core State Standards for Mathematics, and NCTM Principles and Standards for School Mathematics. The lessons are organized around the statistical problemsolving process in the GAISE guidelines: formulate a statistical question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the data in the context of the original question. Teachers can navigate the STEW lessons by grade level and statistical topic.