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Department of Statistics, UCLA

Other Recent Work bannerUCLA

Other Recent Work

The Department of Statistics at UCLA coordinates undergraduate and graduate statistics teaching and research within the College of Letters and Sciences. We teach a large number of undergraduates and we have a substantial graduate program. Our research and teaching have a strong emphasis on computational and applied statistics.

Cover page of Data Analysis Activities and Problems for the Computer Science Major in a Post-calculus Introductory Statistics Course

Data Analysis Activities and Problems for the Computer Science Major in a Post-calculus Introductory Statistics Course

(2011)
Cover page of Application of "Case Based Approach" Along with "Generative Model of Teaching" and "Technical Writing" to the Teaching of Applied Statistics

Application of "Case Based Approach" Along with "Generative Model of Teaching" and "Technical Writing" to the Teaching of Applied Statistics

(2011)

WHAT PROBLEM DO WE ASPIRE TO SOLVE? We want to walk away from the traditional overview of statistics as a discipline that reliesupon repetitive procedures with fictitious datasets and major emphasis on step-wise and structured procedures.

INSTEADWe want to present applied statistics as aninterdisciplinary approach that allows thestudents to use statistics to answer real world questions and communicate statistical results.

HOW ARE WE APPROCAHING THIS DILEMMA?Implementation of case-based approach along with "generative model of teaching" and "technical writing"

Cover page of Interactive Correspondence Analysis in a Dynamic Object-Oriented Environment

Interactive Correspondence Analysis in a Dynamic Object-Oriented Environment

(2011)
Cover page of The Causal Mediation Formula – A practitioner guide to the assessment of causal pathways

The Causal Mediation Formula – A practitioner guide to the assessment of causal pathways

(2011)

Recent advances in causal inference have given rise to a general and easy-to-use estimator for assessing the extent to which the effect of one variable on another is mdiated by a third. This estimator, called Mediation Formula, is applicable to nonlinear models with both discrete and continuous variables, and permits the evaluation of path-specific effects with minimal assumptions regarding the data-generating process. We demonstrate the use of the Mediation Formula in simple examples and illustrate why parametric methods of analysis yield distorted results, even when parameters are known precisely. We stress the importance of distinguishing between the necessary and sufficient interpretations of “mediated-effect” and show how to estimate the two components in nonlinear systems with continuous and categorical variables.

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