Resource Library

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.
• A 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. 

The Military Spending lab uses interactive, online graphs to better understand total military spending for each country. We see the limitations of traditional histograms and also consider the importance of using appropriate scales when comparing countries.  The emphasisis of this lab is on understanding the impact of appropriate data transformations and data visualizations.

App:  http://shiny.grinnell.edu/Military_Spending_Basic/

Handout:  http://web.grinnell.edu/individuals/kuipers/stat2labs/Handouts/MilSpendB...

The NYPD lab uses interactive, online graphs to better understand patterns in stop and arrest data for the New York Police Department. These data were originally collected by New York Police Department officers and record information gathered as a result of stop question and frisk (SQF) encounters during 2006. These data were used in a study carried out, under contract to the New York City Police Foundation, by the Rand Corporation's Center on Quality Policing. The release of the study, "Analysis of Racial Disparities in the New York Police Department's Stop, Question, and Frisk Practices" (Rand Document TR-534-NYCPF, 2007) generated interest in making the data available for secondary analysis. This data collection contains information on the officer's reasons for initiating a stop, whether the stop led to a summons or arrest, demographic information for the person stopped, and the suspected criminal behavior."

The Global Terrorism Database (GTD) contains information about more than 140,000 terrorist incidents occurring between 1970 and 2014. The data in the GTD are gathered from information gathered through multiple news sources (LaFree, Dugan, & Miller, 2015). In this activity, we will study the extent to which chemical, biological, radiological, and nuclear (CBRN) weapons have been used so far. We analyze whether or not their past use fits with our perceptions. Have CBRN weapons been used successfully in the past? Which weapons are more historically dangerous (more fatalities, injuries) in the hands of terrorists? What are the implications of past usage of CBRN weapons compared to other weapons in determining our priorities in counter-terrorism policies?

As mentioned on the home page of this resource "This site presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. This material is designed to give students a sense of the importance and allure of statistics early in their college career. By incorporating many of the successful reforms of the introductory statistics course into a wide range of more advanced topics we hope that students in any discipline can realize the intellectual content and broad applicability of statistics."

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.

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).

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.

This applet builds confidence intervals for the percentage of orange candies in box with two colors of candies. A smaller box visualizes the sample, and a graph keeps track of the location of the confidence interval. Students can take one sample (producing one CI) repeatedly, or take 100 random samples at once. The population percentage is hidden from view unless the student asks to see it, in which case it is displayed on the graph of confidence intervals. This allows the students to see whether each interval "hits" or "misses". Several parameters can be varied: sample size, confidence level and number of samples. A set of questions alongside the applet guides students.

OStats is a simple tool for data visualisation and statistical analysis, particularly aimed at helping students learn statistics.