What factors are most responsible for height?

Model Specification

ERROR???measurement errormodel erroranalysis

unexplainedunknownunaccounted formissing variables

Outcome = (Model) + Error

Analytics & History: 1st Regression Line

The first “Regression Line”

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

Galton’s Notebook on Families & Height

X1 X2 X3 X4 X5 Y

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

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

> setwd()

Dataset Input

Function FilenameObject

Data Types: Numbers and Factors/Categorical

str() summary()

head() summary()

ece

ece

Continuous Categorical

Continuous

Categorical

Histogram

Scatter

Bar

CrossTable

Boxplot

Predictor Variable(X-Axis)

Pie

Child’s Height

Smartphone?Yes or No

Yes No

Outcome, Dependent Variable

(Y-Axis)

Mosaic

CrossTable

LinearRegression

LogisticRegression

Regression Model

Parents Height

Gender

Frequency

01

Outcome, Dependent Variable

(Y-Axis)

Frequency Distribution, Histogramhist(heights$childHeight)

Standard Deviation

0 1 2 3 4 5 6 762

64

66

68

70

72

74

Mean

• Deviation between mean and an actual data point.

Calculating Standard Deviation - sd()

Normal Distribution and SD

Mean = 66.5S.D. = 3.6

66.5 + 7 = 73.6

SD Pct. Z-score Heights

1 90% 1.64

2 95% 1.96 7.06

3 99% 2.58

66.5 - 7 = 59.4

Area = 1

Density Plot

plot(density(h$childHeight))

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

ggplot2

ggplot2