Fitting Percentage of Body Fat to Simple Body Measurements: College Women

References

• Abraham, B., and Ledolter, J. (2006), Introduction to Regression Modeling, Belmont, CA: Thomson Brooks/Cole.
   Google Scholar
• Bailey, C. (1991), The New Fit or Fat, Boston, MA: Houghton-Mifflin.
   Google Scholar
• Binnie, N. (2004), “Using EDA, ANOVA and Regression to Optimise Some Microbiology Data,” Journal of Statistics Education, 12. DOI: https://doi.org/10.1080/10691898.2004.11910738.
   Google Scholar
• Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984), Classification and Regression Trees, Boca Raton, FL: Chapman and Hall/CRC Press.
   Google Scholar
• Carter, N., Felton, N., and Schwertman, N. (2014), “A Classroom Investigation of the Effect of Population Size and Income on Success in the London 2012 Olympics,” Journal of Statistics Education, 22. DOI: https://doi.org/10.1080/10691898.2014.11889698.
   Google Scholar
• Centers for Disease Control and Prevention. (2021b), https://www.cdc.gov/obesity/adult/defining.html.
   Google Scholar
• Centers for Disease Control and Prevention. (2021a), Available at https://www.cdc.gov/obesity/data/obesity-and-covid-19.html.
   Google Scholar
• Chandra, P., Singh, A. P., and Sehrawat, R. (2016), “Effect of Activation Function Symmetry on Training of SFFANNs with the Backpropagation Algorithm,” 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, 2016. DOI: https://doi.org/10.1109/ISCO.2016.7726903.
   Google Scholar
• Chatterjee, S., and Hadi, A. S. (2012), Regression Analysis by Example (5th ed.), New York: John Wiley & Sons.
   Google Scholar
• Deurenberg, P., and Yap, M. (1999), “The Assessment of Obesity: Methods for Measuring Body Fat and Global Prevalence of Obesity,” Best Practice & Research Clinical Endocrinology & Metabolism, 13, 1–11.
   PubMed Web of Science ®Google Scholar
• Draper, N. R., and Smith, H. (1998), Applied Regression Analysis (3rd ed.), Wiley-Interscience.
   Google Scholar
• Elagizi, A., Kachur, S., Lavie, C.J., Carbone, S., Pandey, A., Ortega, F. and Milani, R.V. (2018), “An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases,” Progress in Cardiovascular Diseases, 61, 142–150. DOI: https://doi.org/10.1016/j.pcad.2018.07.003.
   PubMed Web of Science ®Google Scholar
• Faraway, J. J. (2014), Linear Models with R (2nd ed.), Boca Raton, FL: CRC Press.
   Google Scholar
• Finer, N. (2015), “Medical Consequences of Obesity,” Medicine, 43, 88–93. DOI: https://doi.org/10.1016/j.mpmed.2014.11.003.
   Google Scholar
• Garcia, F., and Espinosa, J. (2013), “Estimation of Body Fat Percentage Using Neural Networks,” Links Magazine, 10.
   Google Scholar
• Hartenian, E., and Horton, N. (2015), “Rail Trails and Property Values: Is There an Association?,” Journal of Statistics Education, 23. DOI: https://doi.org/10.1080/10691898.2015.11889735.
   Google Scholar
• Holcomb, J., and Spalsbury, A. (2005), “Teaching Students to Use Summary Statistics and Graphics to Clean and Analyze Data,” Journal of Statistics Education, 13. DOI: https://doi.org/10.1080/10691898.2005.11910567.
   Google Scholar
• Johnson, R. W. (1996), “Fitting Percentage of Body Fat to Simple Body Measurements,” Journal of Statistics Education, 4, http://jse.amstat.org/v4n1/datasets.johnson.html, dataset: http://jse.amstat.org/datasets/fat.dat.txt, variable descriptions: http://jse.amstat.org/v4n1/datasets.johnson.html, dataset: http://jse.amstat.org/datasets/fat.dat.txt, variable descriptions: http://jse.amstat.org/datasets/fat.txt.
   Google Scholar
• Johnson, R. W. (2020), Least Squares & Regression: The Basics Using R. Available at https://leanpub.com/least_squares_regression/.
   Google Scholar
• Kuiper, S. (2008), “Introduction to Multiple Regression: How Much is Your Car Worth?” Journal of Statistics Education, 16. DOI: https://doi.org/10.1080/10691898.2008.11889579.
   Google Scholar
• Lock, R. H., Lock, P. F., Morgan, K. L., Lock, E. F., and Lock, D. F. (2017), Unlocking the Power of Data (2nd ed.), New York: John Wiley & Sons.
   Google Scholar
• Lohman, T. G., Roche, A. F., and Martorell, R., Editors (1988), Anthropometric Standardization Reference Manual, Champaign, IL: Human Kinetics Books.
   Google Scholar
• Manly, B.F.J. and Navarro-Alberto, J.A. (2017), Multivariate Statistical Methods: A Primer (4th ed.), Boca Raton, FL: CRC Press.
   Google Scholar
• Matloff, N. (2017), Statistical Regression and Classification: From Linear Models to Machine Learning, Boca Raton, FL: CRC Press.
   Google Scholar
• Mills, T. C. (2005), “Predicting Body Fat using Data on the BMI,” Journal of Statistical Education, 4.
   Google Scholar
• Mooney, D. D. and Swift, R. J. (1999), A Course in Mathematical Modeling, Mathematical Association of America.
   Google Scholar
• Olive, D. J. (2017), Linear Regression, Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Peterson, A., and Ziegler, L. (2021), “Building a Multiple Linear Regression Model with LEGO Brick Data,” Journal of Statistics Education, https://doi.org/https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1946450.
   Web of Science ®Google Scholar
• Pischon T., Nimptsch K. (2016), Obesity and Risk of Cancer: An Introductory Overview (chapter). In Recent Results in Cancer Research (book series), Obesity and Cancer (vol. 208), eds. T. Pischon and K. Nimptsch, Cham, Switzerland: Springer International Publishing.
   Google Scholar
• R Core Team (2020), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria https://www.R-project.org/.
   Google Scholar
• Slack, Jason V. (1997), “Estimating Body Fat Percentage Using Circumference Measurements and Lifestyle Questionnaire Data: A Multivariate Study of 184 College Aged Females,” M.S. Thesis, Department of Physical Education, Brigham Young University. Available via OregonPDF in Health & Performance, https://www.oregonpdf.org/, Catalog ID: PE 3794.
   Google Scholar
• Slack, Jason V. (2020, 2021), Personal communications.
   Google Scholar
• Wasserstein, R. L., and Lazar, N.A. (2016), “The ASA Statement on p-Values: Context, Process, and Purpose,” The American Statistician, 70, 129–133.
   Web of Science ®Google Scholar
• Witt, G. (2013), “Using Data from Climate Science to Teach Introductory Statistics,” Journal of Statistics Education, 21. DOI: https://doi.org/10.1080/10691898.2013.11889667.
   Google Scholar