2.1

7

  1. China

  2. 50 million

  3. 350 million

  4. China’s population is much higher than the other countries, which means it will most likely have a higher frequency. It’s better to use relative frequency here.

9

  1. 69%

  2. 55.2 million

  3. Inferential, because the statement is based off of information from the survey after it was conducted.

11

  1. 0.42; 0.61

  2. 55+

  3. 18-34

  4. The older age groups are more likely to purchase goods if they’re made in America, as opposed to the younger audience, who are less likely to purchase goods if they’re made in America.

13

Never: 0.0262

Rarely: 0.0678

Sometimes: 0.1156

Most of the time: 0.2632

Always: 0.5272

  1. 52.7%

  2. 9.4%

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. Inferential, because it is taking the results of a sample and applying it as a generalization to the rest of the population.

15

More then 1 hour: 0.3678

Up to 1 hour: 0.1873

A few time a week: 0.1288

A few times a month: 0.0790

Never: 0.2371

  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. The claim is taken from data of a random sampling so it should not be used as a reference without additional research.

2.2

9

  1. 8

  2. 2

  3. 15 times

  4. 4

  5. 15%

  6. Bell shaped distribution

10

  1. 4

  2. 9

  3. 17.3%

  4. Skewed right

11

  1. 200

  2. 10

  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. 320

  2. Skip this problem

  3. 0-199

  4. Skewed right

  5. This statement does not take into account any other possible factors that could cause traffic fatalities besides alcohol. However, we can conclude from this statement that there is a less likelihood of an alcohol-related traffic fatality occuring in Vermont as opposed to Texas.

13

  1. Likely skewed right; the majority of Americans do not make as much as wealthy Americans, who make money in the millions.

  2. Likely bell-shaped; The average student is more likely to have a score on a standardized exam in the middle range, with much fewer scoring really low and really high scores.

  3. Likely skewed right; Most households will consist of fewer people on average, with the number of households exceeding 4 diminishing in the histogram.

  4. Likely skewed left; Most people with Alzheimer’s disease are older, so the histogram will show the numbers being significantly fewer with the younger demographic.

14

  1. Likely skewed left; More people are likely to drink less since they would not want to exceed their limits.

  2. Likely skewed right; The overwhelming majority of students in a public school district are young.

  3. Likely skewed left; Patients with hearing-aids are generally older.

  4. Likely bell-shaped; The number of full grown men with an average height are likely to fall in the middle range of a histogram, with the number who are above average or below average diminishing.