# June 2: Statistics in Practice

Fear of catching Covid-19 dominates the world, so this week we briefly review how humans think about probabilities, in the context of Covid-19.  Prior beliefs figure heavily in probability calculations, so our course spotlight is on:

See you in class!

P.S.  Our new course, Analyzing and Modeling Coronavirus Data, starts June 12

Founder, Author, and Senior Scientist

# When Probabilities Sum to More than One

Fear of catching Covid-19 has captivated the world, and paralyzed it sufficiently to throw nearly every country into a deep recession. Is it a reasonable fear? Humans are known to be poor judges of probabilities, especially for unlikely events – people routinely[…]

# Word of the Week

## Bayesian Statistics

Bayesian statistics provides probability estimates of the true state of the world. An unremarkable statement, you might think -what else would statistics be for? But classical frequentist statistics, strictly speaking, only provide estimates of […]

# Mastery Spotlight

## Bayesian Statistics

Our Mastery in Bayesian Statistics includes 5 course options from which you pick 3. After completing an introductory course, you will learn how to implement Markov Chain Monte Carlo (MCMC) estimation methods, and use open source software (BUGS) for Windows and/or R.

The introductory course in the sequence starts July 3, and is taught by William Bolstad, author of the text An Introduction to Bayesian Statistics (Wiley).

The more advanced courses in Bayesian computing follow during the remainder of the year, and are taught by Peter Congdon, author of Bayesian Statistical Modeling and other texts.

See you in class!

# Course Spotlight

## Analyzing and Modeling Covid-19 Data (June 12 to July 10)

We’ll cover analysis of Covid data broadly, and focus on the epidemiological and statistical models used to forecast the spread of the pandemic. In this seminar-style course for statistically-literate* researchers, you will

• Explore key rates and features of the Coronavirus data
• Learn how to specify epidemiological models
• Learn how to fit statistical models
• The strengths and weaknesses of each type of model

The instructors are

• James Hardin, Epidemiology and Biostatistics Associate Professor and Associate Dean for Faculty Affairs and Curriculum for the Arnold School of Public Health, University of South Carolina.
• Wayne Folta a Lead Data Scientist at Elder Research, Inc. where he develops and deploys models for clients. His current work involves the design and implementation of text mining and deep learning models at the U.S. Dept. of Health and Human Services; he has also been active within Elder Research in exploring and assessing epidemiological models in R.

See you in class!