1.1

1. Statistics is the science of collecting, organizing, summarizing, and analyzing information to draw conclusions or answer questions. In addition, statistics is about providing a measure of confidence in any conclusions.

3. Individual

5. Statistic, Parmeter

7. Parameter (summary of population of governors in US)

9. Statistic (summary of the sample of HS students)

11. Parameter

13. Statistic

15. Qualitative

17. Quantitative

19. Quantitative

21. Qualitative

23. Discrete

25. Continuous

27. Continuous

29. Discrete

39 Population: all US teenagers age 13-17 Sample: 1,028 US teenagers age 13-17

40 Population: all bottles of soda from Oct 15 Sample: 50 bottles of soda from Oct 15

41 Population: all plants of soybeans Sample: 100 plants of soybean

42 Population: all US households Sample: 50,000 US households

1.2

11. Experiment

13. Observational

2.1

7

  1. China

  2. 50 million

  3. 350 million

  4. The graph represents the Top 10 Internet Users by frequency and not by percentage of users within the context of that countries population, which could be misleading.

9

  1. 69%

  2. 55.2%

  3. Descriptive, because it just summarizes data.

11

  1. 45%, 61%

  2. 55+

  3. 18-34

  4. The older you are the more likely you are to buy something when it has been made in America

13

Never: .0261

Rarely: .0678

Sometimes: .1156

Most of the time: .2632

Always: .5272

  1. 52.72%

  2. 2.61%, 6.78%

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 uses a sample to make generalizations.

15

More then 1 hour: .367

Up to 1 hour: .187

A few time a week: .128

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. They are taking results based on a sample and generalizing it.

2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. bell-shaped distribution

10

  1. 4

  2. 9

  3. 9%

  4. Bell-shaped

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. The data is not put into the perspective of the population size or miles traveled, a fair comparison can be made by comparing percentages that are relative to population or miles traveled not simply frequency.

13

  1. Skewed right, most incomes will be below the median

  2. Bell shaped, most scores will be close to the median with balanced scores on either side.

  3. Skewed right, most households will have a number of occupants below the median.

  4. Skewed left, most alzheimer’s patients ages will be above the median.

14

  1. Skewed right, most people will consume a number of alcoholic drinks that is below the median.

  2. Uniform, most grades with respective ages will have the same amount of students throughout the district.

  3. Skewed left, most hearing-aid patients ages will be above the median.

  4. Bell shaped, most heights will be close to the median with balanced heights on either side.