## Homework 5

Enter your name and EID here

This homework is due on Mar. 5, 2019 at 4:00pm. Please submit as a PDF file on Canvas.

For this homework, you will work with a dataset collected by John Holcomb from the North Carolina State Center for Health and Environmental Statistics. This data set contains 1409 birth records from North Carolina in 2001.

NCbirths <- read_csv("http://wilkelab.org/classes/SDS348/data_sets/NCbirths.csv")
## Parsed with column specification:
## cols(
##   Plural = col_integer(),
##   Sex = col_integer(),
##   MomAge = col_integer(),
##   Weeks = col_integer(),
##   Gained = col_integer(),
##   Smoke = col_integer(),
##   BirthWeightGm = col_double(),
##   Low = col_integer(),
##   Premie = col_integer(),
##   Marital = col_integer()
## )
head(NCbirths)
## # A tibble: 6 x 10
##   Plural   Sex MomAge Weeks Gained Smoke BirthWeightGm   Low Premie Marital
##    <int> <int>  <int> <int>  <int> <int>         <dbl> <int>  <int>   <int>
## 1      1     1     32    40     38     0         3147.     0      0       0
## 2      1     2     32    37     34     0         3289.     0      0       0
## 3      1     1     27    39     12     0         3912.     0      0       0
## 4      1     1     27    39     15     0         3856.     0      0       0
## 5      1     1     25    39     32     0         3430.     0      0       0
## 6      1     1     28    43     32     0         3317.     0      0       0

The column contents are as follows:

• Plural: 1=single birth, 2=twins, 3=triplets.
• Sex: Sex of the baby 1=male 2=female.
• MomAge: Mother’s age (in years).
• Weeks: Completed weeks of gestation.
• Gained: Weight gained during pregnancy (in pounds).
• Smoke: Mother is a smoker: 1=yes, 0=no.
• BirthWeightGm: Birth weight in grams.
• Low: Indicator for low birth weight, 1=2500 grams or less, 0=otherwise.
• Premie: Indicator for premature birth, 1=36 weeks or sooner, 0=otherwise.
• Marital: Marital status: 0=married or 1=not married.

Problem 1 (3 pts): We are interested in assessing the relationships between the variables in the dataset NCbirths and the mothers’ marital status, the mothers’ smoking habits, and plural births. Perform a principal components analysis (PCA) on the dataset NCbirths. Remove the columns Marital, Smoke, and Plural prior to performing PCA. Create a scatterplot of PC1 vs. PC2. First, color each point bythe mother’s marital status, then color each point by the mother’s smoking habit, and then color each point by the indicator of plural births. What do you observe? Visually, and without doing any calculations, do the different types of births cluster together in principal-component space? Do the smokers or married mothers cluster together?

# your R code goes here

Problem 2 (4 pts): Now visualize the rotation matrix of the PCA obtained under Problem 1.

# your R code goes here

Given the plots from Problem 1 and the arrow plot you made, how do you interpret PC1 and PC2? What does PC1 tell you about a data point? What does PC2 tell you about a data point?

# your R code goes here