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IN PERSON – 5-day Statistics Short Course

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Seminar Overview:

This is a comprehensive five day Seminar on Bayesian Data Analysis. This is our most popular training event each year where students and professionals have the opportunity to learn directly from a vetted statistics instructor. The live lectures and one-on-one Q & A provide a rare chance to bring your own personal research and let us help you break through any roadblocks standing in the way of progressing your work. The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Seminar Topics:

  • The rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis
  • The concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule
  • The concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications
  • How to use the free software R for Bayesian analysis.
  • An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, logistic regression, multilevel regression, and growth models, count regression, robust regression, regularization and polynomials, and missing data treatments in Bayesian inference.

Seminar Description:

Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. This seminar shows you how to do Bayesian data analysis, hands on.

Instructor: Mauricio Garnier-Villarreal, Ph.D.

Mauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods, but also in the application of these in data; in different fields like special education, cognitive decline in aging, healthy aging (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011.

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Instructor: Esteban Montenegro, Ph.D.

I’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R, and I love learning about Linux and statistical new tools.I’m always open to new projects and ideas, I collaborate with several teams around the world on topics related to healthy aging, Alzheimer Disease and other health related topics.

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APA Continuing Education Credits:

This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Describe the fundamental properties of Bayesian reasoning.
  • Implement Bayes’ rule
  • Utilize Markov Chain Monte Carlo in the brms package for R to evaluate Bayesian models.
  • Apply the appropriate prior distribution to a Bayesian analysis.
  • Conduct generalized linear models using Bayesian estimation, including simple linear regression, multiple regression, and robust regression.
  • Conduct a T-test using Bayesian estimation.
  • Conduct a Bayesian ANOVA.
  • Evaluate Logistic and Count regression models using Bayesian estimation.
  • Evaluate Multilevel linear regression models using Bayesian estimation.
  • Conduct a Bayesian Multilevel growth curve analysis.
  • Understand how issues of missing data are addressed in the Bayesian framework.
  • Explain the difference between frequentist and Bayesian statistics
  • Critically evaluate applications of Bayesian analysis in scientific studies.
  • Analyze data using Bayesian techniques in R.
  • Specify, estimate, evaluate, and compare different Bayesian models to fit possible hypothesis.

Participants are encouraged to bring data of interest to work with during the week, there will be time to work on it with help of the instructor. Work on datasets of interest will magnify the learning and impact of the course.

Participants will also complete the course with a foundation for future learning about Bayesian statistics and knowledge about available resources to guide such endeavors.

Seminar Prerequisites:

Required:

  • Basic proficiency in multiple linear regression, the generalized linear model.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multilevel models
  • Intermediate proficiency in R, or syntax base software

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Bayesian Statistics
  • Data analysis using R

Software and Computer Support:

It is important to bring a notebook computer to the seminar, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms.

Seminar Audience:

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis.

Seminar Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.

Instructor will provide password on first day of seminar:
Click Here to Access Bayesian Data Analysis Seminar Files

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Summer Stats Camp 2023: Bayesian Data Analysis
Monday June 5, 2023
9:00–10:45 Bayesian reasoning generally
10:45–11:00 Rest Break
11:00–12:30 Perfidious p values and the con game of confidence intervals
12:30–1:30 Rest Break
1:30–3:15 Bayes’ rule, grid approximation, and R; Simple examples to train intuition
3:15–3:30 Rest Break
3:30–5:00 Markov Chain Monte Carlo
Tuesday June 6, 2023
9:00–10:45 Brief introduction to R and Rstudio
10:45–11:00 Rest Break
11:00–12:30 Know your distributions. Application of priors
12:30–1:30 Rest Break
1:30–3:15 General linear model. Multiple regression, and robust regression
3:15–3:30 Rest Break
3:30–5:00 General linear model. Robust regression
Wednesday June 7, 2023
9:00–10:45 Model comparison
10:45–11:00 Rest Break
11:00–12:30 General linear model. T-test and ANOVA
12:30–1:30 Rest Break
1:30–3:15 Regularization and polynomials in linear regression
3:15–3:30 Rest Break
3:30–5:00 Generalized linear Model. Logistic regression
Thursday June 8, 2023
9:00–10:45 Generalized linear Model: Count regression
10:45–11:00 Rest Break
11:00–12:30 Multilevel linear regression
12:30–1:30 Rest Break
1:30–3:15 Multilevel linear regression
3:15–3:30 Rest Break
3:30–5:00 Multilevel growth curve/repeated measures
Friday June 9, 2023
9:00–10:45 Introduction to missing data
10:45–11:00 Rest Break
11:00–12:30 Missing data in Bayesian inference
12:30–1:30 Rest Break
1:30~3:30 Individual Consultations

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