An Introduction to Bayesian Inference
July 2022: This webinar will provide an introduction to basic concepts in Bayesian inference. Topics that will be covered include essential components of Bayesian statistics, estimation and uncertainty quantification in single and multi- parameter linear and generalized linear models, as well as a brief introduction to Bayesian hierarchical modeling and Bayesian computation. The workshop will include examples of parametric inference in R using R-packages that rely on Stan (rstanarm and brms). At the end of this workshops participants will be able to: 1) Specify simple Bayesian models, 2) Make Bayesian inference in single parameter models, and 3) Fit linear and generalized linear models using rstanarm or brms.
An Introduction to Small Area Estimation
May 2022: Small area estimation (SAE) techniques can lead to greatly improved estimates relative to direct survey estimates when there is a large number of domains of interest and a limited overall sample size, which is often the case in surveys. When successfully applied, SAE can dramatically reduce measures of uncertainty and provide estimates for domains with no survey data. It can allow for publishing of official estimates at lower levels of aggregation. We will discuss the following topics: What is small area estimation (SAE)? What are the potential benefits of SAE? Examples of real applications of small area estimation; An introduction to area-level and unit-level models;; Discussion of frequently used software.
Politics, Product, and Peanut Butter: A Decade in Statistics & Data Science
February 2022: In this SWB networking event, Matt Brems talks about his decade-long career in statistics and data science.
Visualizing data with R & ggplot
December 2021: In this webinar, we learn why line charts are great for time series, how the eye moves across the chart and how to use this information in making visualizations more digestible, and many other tips & tricks on how to make your visuals stand out. We will also cover the thinking behind ggplot, and how supplementary libraries can help you build charts that stand out.
A Nationally Representative Economic Survey 5 Months After the Haitian Earthquake
February 2016: There were many volunteers for this project including those who visited Haiti two months after the earthquake in 2010 and those who conducted the survey five months after the earthquake. Statistics without Borders conducted a nationally representative sample survey to examine economic impact using a random digit dial sample of mobile phone numbers. We analyzed the anonymized survey data and the questionnaire that they made available for public use. Radical changes in household members occurred among post-earthquake Haitian households. Similar changes of household members that are caused by natural disasters have been associated with long-term psychological well-being in the literature. The survey also provides a rare look at gender discrepancy in employment retention following a natural disaster from a nationally representative survey. While the overall employment rate was down by 50% five months after the earthquake, our findings indicate that households with female heads are at a significantly greater risk of losing employment.
Healthy Babies and Mothers Program in Myanmar
October 2015: Prenatal care plays a critical role in material and infant healthcare. The present work seeks to assess a maternal and infant care program administered by Global Community Service Foundation, (GCSF), in the Inle lake area of Myanmar (formerly called Burma), and find ways to expand it. Such expansions includes identification of critical maternal and infant care knowledge gaps among women and health care workers with the objective of communicating those gaps so that they can be addressed. Statistics Without Borders (SwB) members worked closely with the organization to develop a study, which fits the purpose and meets the ultimate objective. This paper discusses the key results and issues of this collaborative work.
Typhoon Haiyan Tweet Analysis
September 2015: The disaster response resources and the analytical resources must work closely together to ensure that the analysis is fit for purpose and meets the ultimate objective. This study discusses the key considerations for such collaboration through an analysis of Twitter data surrounding the 2013 landfall of Typhoon Haiyan in the Philippines.