# Resource Library

#### Statistical Topic

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• ### Data 8: The Foundations of Data Science

This UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? This course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

• ### Web Interface for Statistics Education (WISE)

The goal of WISE is to provide students and teachers of statistics easy access to a wide range of resources that are freely available on the internet. We invite you to explore our website and enjoy many wonderful statistical materials from around the world.

• ### Normal v. Binomial Distribution (Shiny App)

Approximating a normal distribution with a binomial distribution

• ### 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.

• ### Find-a-fit! (Shiny App)

Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

• ### Polynomial Surface Explorer (Shiny App)

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

• ### When does a significant p-value indicate a true effect? (Shiny App)

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

• ### Feeling Bayes Factor: Height Difference Between Males and Females (Shiny App)

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?

• ### What does a Bayes factor look like? [The urn model] (Shiny App)

Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.

• ### True Scientific Discoveries: What Research Approach is Most Cost-Effective? (Shiny App)

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