1. Z score 34-week baby: (2400-2600)/660=-.303

Z score 40-week baby: (3300-3500)/470=-.426

The 40-week baby weighs less relative to the gestation period

  1. Z score man: (75-69.6)/3= 1.8

Z score woman: (70-64.1)/3.8=1.55

The 75-inch man is relatively taller

  1. Z score Johnson: (2.30-3.622)/.743=-1.78

Z score Herdandez: (2.27-3.929)/.775=-2.14

Johnson did better relative to his peers because his Z score was closest to 0 (the average z score)

  1. Z score Franchitti (185.62-186.15)/.359=-1.48

Z score Power (112.57-112.8)/.131=-1.76

Power had the more convincing victory

  1. 1.5=(x-200)/26 x=239

  1. 15% of males from 3-5 months have a head circumference of 41.0cm or less
  2. 90% of 2-year-old females have a waist circumference of at least 52.7cm
  3. I might conclude that men ages 20-49 tend to have higher standing heights than men who are ages 50+
  1. Mean: 10.08

Squared deviations 204.49
123.21 108.16 88.36 68.89 151.29 112.36 102.01 86.49 50.41 148.84 110.25 100 81 49 127.69 108.16 94.09 77.44 43.56

mean=101.79/19= 5.36 (one standard deviation) Blackie’s Z score (7.8-10.08)/5.36=-.43

5.7, 7.7, 7.8, 8.7, 8.9, 9.4, 9.5, 9.6, 9.6, 9.9, 10.0, 10.3, 10.6, 10.7, 11.0, 11.2, 11.7, 12.9, 13.0, 13.4 Q1= 9.15 Q2=9.95 Q3= 11.45

Q3-Q1=2.3 9.15-1.5(2.3)=5.7 11.45+1.5(2.3)=14.9 There are no outliers

  1. 345, 346, 358, 372, 429, 437, 442, 442, 461, 466, 466, 470, 471, 480, 489, 490, 505, 515, 516, 549 Q1= 433 Q2=466 Q3= 489.5 489.5+1.5(56.5)=574.25
  1. The distribution is right skewed
  2. 0, 1, 3, 6, 16
  1. The distribution is symmetric
  2. 0, 2, 5, 8, 11
  1. Skewed right
  2. 52
  3. Y, because the spread extends much farther throughout the number line
  4. Symmetric, because the data is evenly distributed on both sides
  5. Skewed right because most of the data is concentrated on the left side
  1. 16
  2. 22
  3. Y, because the spread extends much farther throughout the number line
  4. Yes, 30
  5. Left skewed, because most of the data is concentrated on the left side
my_data <- c(60, 63, 68, 68, 68,
               75, 75, 77, 79, 89, 
               89, 89, 93, 94, 98)
             
boxplot(my_data, horizontal = T)

fivenum(my_data)
## [1] 60 68 77 89 98

8

my_data <- c(110, 125, 140, 140,140,
               150, 152, 157, 160, 173, 
               173, 173, 180, 180,205)
             
boxplot(my_data, horizontal = T)

fivenum(my_data)
## [1] 110 140 157 173 205

9

my_data <- c(42, 43, 46, 46, 47,
             47, 48, 49, 49, 50,
             50, 51, 51, 51, 51,
             52, 52, 54, 54, 54,
             54, 54, 55, 55, 55,
             55, 56, 56, 56, 57,
             57, 57, 57, 58, 60,
             61, 61, 61, 62, 64,
             64, 65, 68, 69)

fivenum(my_data)
## [1] 42.0 50.5 54.5 57.5 69.0
boxplot(my_data, horizontal = T)

  1. Five Number summary:42.0 50.5 54.5 57.5 69.0

  2. The data is very slightly left skewed, but relatively symmetric

10

my_data <- c(7.2, 7.8, 7.8, 7.9, 8.1, 8.3,
              8.5, 8.6, 8.6, 8.6, 8.7, 8.8,
              9.0, 9.1, 9.2, 9.2, 9.2, 9.4,
              9.4, 9.6, 9.7, 9.7, 9.9, 9.9,
              10.0, 10.0, 10.0, 10.1, 10.2,10.3,
              10.3, 10.3, 10.3, 10.7, 10.7, 10.9,
              11.2, 11.2, 11.2, 11.3, 11.3, 11.3,
              11.5, 11.5, 11.7, 12.4, 12.5, 13.6,
              13.8, 14.4, 16.4)


fivenum(my_data)
## [1]  7.20  9.05 10.00 11.20 16.40
boxplot(my_data, horizontal = T)

  1. Five number summary:7.20 9.05 10.00 11.20 16.40

C. The data is right skewed