Title

Fitness Assessment through Body Fat Prediction

Presenter Information

Madelyne Weber

Document Type

Oral Presentation

Location

SURC Ballroom C/D

Start Date

15-5-2014

End Date

15-5-2014

Keywords

Fitness, Body fat, Regression

Abstract

Health and fitness are important to the students at CWU. Even while attending school, students can still maintain a healthy lifestyle. Fitness is known to be correlated with the amount of body fat a person has. With this as the underlying assumption, the data collected by A. Garth Fisher from Brigham Young University will be used to illustrate a way body fat can be predicted without the traditional visit to the personal trainer or doctor’s office. The data collected included over 250 men and took 18 different measurements of the body including two types of body fat measurement. This study will map the data and find any relationships between body measurements and body fat. By modeling on a small scale and building into a large overall predictive model, the correlations between physical measurements and percentage of body fat will be clear. From those findings, a simple to use equation can be derived to predict body fat based on the input measurements. Through regression analysis the assumptions made using statistical theories will be validated, showing that measuring body fat can be a simple at home process.

For this presentation, Madelyne Weber received a College of the Sciences Best Poster Presentation Award for 2014.

Poster Number

49

Faculty Mentor(s)

Chueh, Yvonne

Additional Mentoring Department

Actuarial Science

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May 15th, 8:30 AM May 15th, 11:00 AM

Fitness Assessment through Body Fat Prediction

SURC Ballroom C/D

Health and fitness are important to the students at CWU. Even while attending school, students can still maintain a healthy lifestyle. Fitness is known to be correlated with the amount of body fat a person has. With this as the underlying assumption, the data collected by A. Garth Fisher from Brigham Young University will be used to illustrate a way body fat can be predicted without the traditional visit to the personal trainer or doctor’s office. The data collected included over 250 men and took 18 different measurements of the body including two types of body fat measurement. This study will map the data and find any relationships between body measurements and body fat. By modeling on a small scale and building into a large overall predictive model, the correlations between physical measurements and percentage of body fat will be clear. From those findings, a simple to use equation can be derived to predict body fat based on the input measurements. Through regression analysis the assumptions made using statistical theories will be validated, showing that measuring body fat can be a simple at home process.

For this presentation, Madelyne Weber received a College of the Sciences Best Poster Presentation Award for 2014.