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Determinants of Human Height: Genetics, Nutrition, and Other Factors Explored

This research delves into the factors influencing a person's height. Hypotheses explore genetics, nutrition, and immigration origins, while literature reviews suggest relationships between parental height and offspring height. Analysis reveals the impact of variables such as genes, early life, illnesses, and socioeconomic statuses. Linear regression models and correlation matrices are utilized to investigate these influences. Key findings highlight gender as a significant factor, with parents' height playing a lesser role. Recommendations for future research include analyzing contemporary datasets and location-specific studies.

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Determinants of Human Height: Genetics, Nutrition, and Other Factors Explored

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  1. Research Question What determines a person’s height?

  2. Hypothesis Brainstorming • Genetics • Nutrition • Immigration / Origins • Disease Hypotheses: • Sons will be similar to their Dad’s height • Daughters will be similar to their Mom’s height

  3. Literature Review: Article #1 Francis Galton • Invented Regression • When Mid-Parents are taller then mediocrity, their Children tend to be shorter than they • When Mid-Parents are shorter than mediocrity, their Children tend to be taller then they

  4. Literature Review: Article #2 • Variables: • Genes • First two years of life • Illnesses • Infant mortality rates • Smaller Families • Higher income • Better education

  5. Literature Review: Article #3 “we find that a 54-loci genomic profile explained 4–6% of the sex- and age-adjusted height variance” “the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance”

  6. Literature Review: Summary

  7. Variables Dependent Variable Y Y Height X4 X3 X1 X2 X’s Independent Variables

  8. Height Dataset Variables heights <- read.csv("GaltonFamilies.csv")

  9. Dataset Variables: Type Data Types: Numbers and Factors/Categorical

  10. Summary Statistics

  11. Frequency Distribution, Histogram hist(heights$childHeight)

  12. Mode, Bimodal hist(h$childHeight,freq=F, breaks =25, ylim = c(0,0.14)) curve(dnorm(x, mean=mean(h$childHeight), sd=sd(h$childHeight)), col="red", add=T) Bimodal: two modes

  13. Q-Q Plot

  14. Correlation Matrix for Continuous Variables PerformanceAnalytics package chart.Correlation(num2)

  15. Correlations Matrix: Both Types Zoom in on Gender library(car) scatterplotMatrix(heights)

  16. Categorical: Revisit Box Plot Correlation will depend on spread of distributions Note there is an equation here: Y = mx b

  17. Children Height by Gender

  18. Linear Regression: Model 1 Child’s Height = f(Father’s Height)

  19. Linear Regression: Model 2 Child’s Height = f(Father’s Height) model.5 <- lm(childHeight~gender, data = h)

  20. Linear Regression: Additional Models • Mom • MidParent Height

  21. Compare Models

  22. Discussion Summary • Key Findings: • Gender was the biggest factor • Parents height played a lesser role • Downsides • DataSet used did not include more variables of interest • DataSet for X Country for 1877

  23. Future Research • Include More Predictor Variables • Literature review of a few articles suggests several important factors: • Nutrition • Analyze a Contemporary DataSet • DataSet used was from 18?? • Location Specific as Well

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