2.1

7

  1. China

  2. 50 million

  3. About 350 million

  4. Need relative frequency

9

  1. 69%

  2. 55.2 million

  3. Inferential; uses a sample to project to population

11

  1. 45% or 45/100; 61% or 61/100

  2. 55+

  3. 18 to 34

  4. Increased likelihood with increased age

13

Never: .0261

Rarely: .068

Sometimes: .116

Most of the time: .263

Always: .527

  1. 53%

  2. 9%

d e f

my_data <- c(125, 324, 552, 1257, 2518)

groups <- c("Never", "Rarely", "Sometimes", "Most", "Always")

barplot(my_data, main = "Wearing Seatbelts", names.arg = groups)

barplot(my_data, main = "Wearing Seatbelts", names.arg = groups, col = c("red","blue","green","yellow", "black"))

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Wearing Seatbelts", names.arg = groups, col = c("red","blue","green","yellow","black"))

pie(my_data, labels = groups, main = "Wearing Seatbelts")

  1. Descriptive

15

  1. more then 1 hour: .368

Up to 1 hour: .187

A few time a week: .129

A few times a month: .079

Never: .237

  1. 23.7%

c d e

my_data <- c(377, 192, 132, 81, 243)

groups <- c("More 1", "Up to 1", "Few times week", "Few times month", "Never")

barplot(my_data, main = "Use the internet", names.arg = groups)

barplot(my_data, main = "Use the internet", names.arg = groups, col = c("red","blue","green","yellow", "black"))

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Use the internet", names.arg = groups, col = c("red","blue","green","yellow","black"))

pie(my_data, labels = groups, main = "Use the internet")

  1. It fails to acknowledge that this is an inferential statement.

2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. Symmetrical/ bell-shaped

10

  1. 4

  2. 2

  3. 20%

  4. Skewed right

11

  1. 200

  2. 10 IQ points

  3. 60-69:2; 70-79:3;80-89:13; 90-99:42; 100-109:58; 110-119:40; 120-129:31; 130-139:8; 140-149:2; 150-159:1

  4. 100-109

  5. 150-159

  6. 5.5%

  7. No

12

  1. 200 fatalities

  2. Skip this problem

  3. 0-199

  4. right-skewed

  5. Does not discuss differences in population for the two states, should calculate relative frequency.

13

  1. Skewed right; a few people make a lot of money

  2. Bell-shaped; biological

  3. Skewed right; there may be a few households with a lot of people

  4. Skewed left; not diagnosed in youth

14

  1. Bell-shaped; natural variation of population, some people drink and some don’t

  2. Uniform; even numbers of kids per grade

  3. Skewed left; mostly elderly patients

  4. Bell-shaped; biological