Introduction to Parametric vs Nonparametric Modeling in Motorcycle Crash Analysis

DESCRIPTION

This lecture explores the concepts of parametric and nonparametric modeling, using the motorcycle dataset from Silverman (1985) as a case study. The dataset examines the effects of stimulated impacts on motorcycles, focusing on the dependent variable of time after impact and the response variable of head acceleration in a post-mortem test object. The session showcases parametric modeling techniques, including scatter plots and polynomial fits, as well as nonparametric methods like regression spline fits, to analyze crash impact effects effectively.

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Mar 18, 2014

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Introduction to Parametric vs Nonparametric Modeling in Motorcycle Crash Analysis

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Presentation Transcript

Lecture 1 INTRODUCTION

Parametric v.s. Nonparametric

Parametric Modeling • Examples
Parametric Modelling • Motorcycle data The motocycle data (Silverman 1985) • Collected to study the crashed effects after the motorcycles hit by a stimulated impact • Dependent variable: time after a stimulated impact with motorcycles • Response variable: head acceleration of a PTMO (post mortem human test object), capturing the crash effects
Parametric Modeling • Scatter Plot
Parametric Modelling • Polynoimal Fits