Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).
Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).
When does a significant p-value indicate a true effect? This app will help with understanding the Positive Predictive Value (PPV) of a p-value.
This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124
The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.
Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?
Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.
Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app.
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.
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:
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 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?
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."