1. What factors are most responsible for height?

2. Model Specification ERROR??? measurement error model error analysis unexplained unknown unaccounted for missing variables Outcome = (Model) + Error

3. Analytics & History: 1st Regression Line The first “Regression Line”

4. Men's average height 'up 11cm since 1870s'

5. Galton’s Notebook on Families & Height

6. X1X2X3 X4 X5Y

7. 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

8. > getwd() [1] "C:/Users/johnp_000/Documents" > setwd()

9. Dataset Input Function Filename Object

10. Data Types: Numbers and Factors/Categorical str() summary()

11. head() summary() ece

12. ContinuousCategorical Continuous Categorical Histogram Scatter Bar Cross Table Boxplot Predictor Variable (X-Axis) Pie Child’s Height Smartphone? Yes or No Outcome, Dependent Variable (Y-Axis) Mosaic Cross Table Linear Regression Logistic Regression Regression Model Parents Height Gender Frequency 0 1 Outcome, Dependent Variable (Y-Axis)

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

14. Standard Deviation Mean

15. Deviation between mean and an actual data point. Calculating Standard Deviation - sd()

16. Normal Distribution and SD Mean = 66.5 S.D. = 3.6 66.5 + 7 = 73.6 SDPct.Z-scoreHeights 190%1.64 295%1.967.06 399%2.58 66.5 - 7 = 59.4

18. Area = 1 Density Plot plot(density(h$childHeight))

19. 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 Mode, Bimodal

20. ggplot2