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Bayesian Analysis
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Please pay to info@starflame.org by Zelle or at
starflame.org/training/bap
. Please mark the tuition type(s) you paid below. Early-bird tuition and student tuition are available two months before class starts. Tuition is refundable two weeks before class starts (A processing fee of $50 will be charged). On-demand option provides materials (Slides, code, and recordings) for asynchronous self-study.
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Regular tuition: $575 + $34.5 (tax) = $609.5
Early-bird tuition: $550 + $33 (tax) = $583.5
Student tuition: $525 + $31.5 (tax) = $556.5
On-demand tuition: $500 + $30 (tax) = $530
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How familiar are you with R
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Interested in learning more courses
Categorical Data Analysis
Multilevel and Panel Data Analysis
Structural Equation Models
Causal Inference
Network Analysis
Bayesian Analysis
Text Analysis
Machine Learning
Other:
Below we will ask several questions to understand your quantitative background so that the instructor could teach accordingly. Your experience with script-based statistical software (Python, R, SAS, SPSS, Stata, etc.) is
I can only use drop-down and/or point-and-click menu．
I can write scripts to manage data, conduct data analysis, and divert both codes and output to a log file.
I can set up loops and write simple programs.
Clear selection
Your experience with R is?
None (e.g., never used it before)
Beginning Level (e.g., read in data and conduct descriptive analysis)
Intermediate Level (e.g., merge and reshape data)
Advanced Level (e.g., write loops and/or functions)
Clear selection
Your math training/background includes
Basics of calculus (e.g., single-variable differentiation and integration
Basics of matrix (e.g., addition, subtraction, multiplication, and inversion)
Basics of probability (e.g., addition rule, multiplication rule, conditional probability, probability density function, cumulative density function, etc.)
Basics of statistics (e.g., mean, variance, binomial distribution, normal distribution, sum of squared errors, mean squared errors, null hypothesis significance testing, confidence intervals, etc.)
Your experience with statistics include (click any topic that applies). Note that when you click a topic, that means not only have your learned something about it, but you've also used it in some venue (e.g., assignments or term papers).
Chi-squared test
Simple (bivarate) regression
Multiple regression
Binary regression (e.g., logit and probit)
Polytomous regression (e.g., ordered regression)
Multilevel regression
Structural equation modeling (e.g., factor analysis and the full-blown SEM)
Bayesian estimation
Machine learning (e.g., penalized regression, decision trees, neural networks)
A copy of your responses will be emailed to the address you provided.
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