Ch. 3.4 #5-15 odd, 22, 25

5 z-score of 34 week gestation period baby is -0.303. while the z-score of a 40 week gestation period baby is -0.426. the 40 week old baby weighs less.

7 a 75-inch male is relatively taller than a 70 inch woman.

9 Josh Johnson comparitevely had a better year relative to his peers as he is only 1.779 standard deviations below the mean.

11 Will Power had the more convincing victory as he finished 1.756 standard deviations below the mean.

13 239 is the minimum test score to be admitted.

15 a. 15% of the sample population of males 3 to 5 months have head circumferences of 41.0cm

  1. 90% of females 2 years of age have waist circumferencesof 52.7cm

  2. Men are 100% likely to get smaller after age 49

22 a. z-score of Blackie’s hemoglobin is -1.209 g/dL

  1. min: 5.7

    Q1: 9.15

    Med: 9.95

    Q3: 11.1

    Max: 13.4

  2. IQR: (11.1-9.15)= 1.95

The range of data is fairly close

  1. LF: 9.15 - 1.5(1.95)= 6.225

    UF: 11.1 + 1.5(1.95)= 14.025

    5.7 is an outlier

25 UF: 490 + 1.5(61)= 581.5

A customer should be contacted after they have used 581.5 minutes.

CH. 3.5 #3-10

3 skewed right

4 symmetric

5 a. 40

  1. 50

  2. variable y has a larger dispersion than variable y as its range is much larger.

  3. symmetric the data seems to fall evenly on both sides.

  4. skewed right, as some of the data points are larger than other numbers in the data set.

6 a. 15

  1. 20

  2. Yes, 30

7 Exam Scores

your_data <- c(60, 68, 77, 89, 98)
             
boxplot(your_data, horizontal = T)

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

8 Reading Speed

your_data <- c(110, 140, 157, 173, 205)
             
boxplot(your_data, horizontal = T)

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

9 a. Min: 42

 Q1:  50.5
 
 Med: 54.5
 
 Q3:  57.5
 
 Max: 69
  1. Age at Innauguration
your_data <- c(42, 50.5, 54.5, 57.5, 69)
             
boxplot(your_data, horizontal = T)

fivenum(your_data)
## [1] 42.0 50.5 54.5 57.5 69.0
  1. The data is roughly symmeteric with an outlier of 69.

10 a. Min: 7.4

  Q1:  9.05
  
  Med: 10.0
  
  Q3:  11.2
  
  Max: 16.4
  1. Carpoolers
your_data <- c(7.2, 9.05, 10, 11.2, 16.4)
             
boxplot(your_data, horizontal = T)

fivenum(your_data)
## [1]  7.20  9.05 10.00 11.20 16.40
  1. The data is skewed to the right and has an outlier at 16.4.