2.1

7

  1. China

  2. 50 million

  3. 350 million

  4. This graph may be misleading because it should use relative frequency. Since China’s population is much larger, it is likely to have a higher frequency just due to its population size

9

  1. 69%

  2. 55.2 million

  3. Inferential

11

  1. 0.42;0.61

  2. 55+

  3. 18-34

  4. The apparent association between age and likelihood to buy when made in America is as age increases, likelihood to buy American also increases

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. This is an inferential statement because it is taking a result of the sample and generalizing it to the population “all college students.”

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% (0.2371)

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. What is wrong about this statement is that it does not have any certainty.

2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. Bell shaped

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. 200

  2. Skip this problem

  3. 0-200

  4. skewed right

  5. This is not a correct statement because although more fatalities occurred in Texas than in Vermont, the fatalities in Texas occurred about 23 times less often than in Vermont. Therefore, a fair comparison can be made between alcohol-related traffic fatalities in Texas versus Vermont by looking at the frequency rather than just the quantity.

13

  1. Histogram of annual household incomes in the United States would be probably skewed right. Perhaps most of the household incomes would be to the left (about $40,000-$140,000), while fewer higher incomes to the right of the histogram (incomes in the millions).

  2. Histrogram of scores on standardized exams such as the SAT would probably be bell-shaped. It would be expected that most of the scores be concentrated in the middle with less amount of scores on either ends.

  3. Histogram of number of people living in a household would probably be skewed right. The majority of households will maybe have 5 occupants or less, while there would be less households with a larger amount of occupants.

  4. Histogram of ages of patients diagnosed with Alzheimer’s disease would probably be skewed left. The majority of Alzheimer’s patients will be in the older-age range and less in the younger category.

14

  1. Histrogram of number of alcoholic drinks consumed per week would probably be uniform because there is theoretically no cluster of weeks in the year that should have a significant increase in alcoholic drink consumption

  2. Histogram of the ages of students in a public school district would probably be uniform because there is most likely an even amount of students in the public school distract across all ages.

  3. Histogram of ages of hearing-aid patients would probably be skewed left. The majority of hearing-aid patients will be in the older-age range and less in the younger category.

  4. Histrogram of of heights of full-grown men would probably be bell-shaped. It would be expected that most of men would be in the middle with average heights and less amount of men on either ends of the spectrum, being either very short or very tall.