3.4

  1. 34-week baby = -.30 40-week baby = -.43 The 40-week baby weighs less relative to gestation period

  2. The 75-inch man is relatively taller.

  3. Kershaw had the better year, witha z-score of -2.3, compared to Hernandez’s z-score of -1.9.

  4. He’s better at the 100-meter race, with a z-score of -3.39, compared to a z-score of -3.05 for the 200-meter race.

  5. 239

15

  1. This means that 15% of males 3 to 5 months old have a head circumference of 41cm or less; 85% have a head circumference greater than 41cm.

  2. This means that 90% of 2-year-old females have a waist circumference less than or equal to 52.7cm; 10% have a waist circumference greater than 52.7cm.

  3. The heights of each percentile generally decrease with age, indicating increasingly tall individuals.

22

  1. (hint the mean = 10.08, standard deviation = 1.885), -1.2

  2. Q1 = 9.15 g/dL Q2 = 9.95 g/dL Q3 = 11.1 g/dL

  3. IQR = 1.95 g/dL In other words, the middle 50% of hemoglobin measurements span a range of 1.95 g/dL, which means that the data are somewhat tightly spread.

  4. Lower fence = 6.225 Upper fence = 14.025 5.7 is the only outlier.

Q1 = 433 Q2 = 466 Q3 = 489.5 IQR = 56.5 Upper Fence = 574.25 The cut-off is 574.25 minutes used per month.

3.5

3

  1. Right skewed.

  2. Min = 0 Max = 16 Q1 = 1 Q2 = 3 Q3 = 6

4

  1. Bell curve/normal distribution.

  2. Min = -1 Max = 12 Q1 = 2 Q2 = 5 Q3 = 8

5

  1. 40

  2. 52

  3. y, because it has a larger interquartile range.

  4. x is bell-shaped, because Q1 and Q3 are the same distance from the median, as are the min and max, making the data symmetric.

  5. y is skewed right, because Q3 is farther from the median than Q1 is, and the max is farther from Q2 than the min is.

6

  1. 16

  2. 22

  3. y, because it has a greater interquartile range.

  4. Yes, it has an outlier of 29.

  5. Variable y is skewed left, because Q1 is farther from the median than Q3 is, and the min is farther from the median than the max is.

7

dat1 <- c(60,68,77,89,98)

boxplot(dat1)

8

dat2 <- c(110,140,157,173,205)

boxplot(dat2)

9

dat3 <- 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)

  1. 42, 51.5, 54.5, 57.5, 69

boxplot(dat3)

  1. The distribution is symmetric.

10

dat3 <- 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,
          10.0, 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)

  1. 7.2, 9, 10, 11.2, 16.4

boxplot(dat3)

  1. Right skewed.