Isa Ruggiero DuMond

1.1

1. Statistics is a numerical study of data and the logical interpretation of that data.

3. An individual is a person or object that is a member of the population being studied

5. A statistic is a numerical summary of a sample, a paramerter is a numerical summary of a population

7. parameter

9. statistic

11. statistic

13. statistic

15. qualitative

17. quantitative

19. quantitative

21. qualitative

23. discrete

25. continuous

27. continuous

29. discrete

39 population: teenagers. sample: Teenagers in the US, ages 13-17. sample size: 1028.

40 population: coke bottles. sample: coke bottles filled on october 17th. sample size: 50.

41 population: soybean crop. sample: randomly selected soybean plants. sample size: 100

42 population: households. sample: households in the USA. sample size: 50,000

1.2

11. experimental

13. observational

2.1

7

  1. china was the country that had the most internet users in 2010. (~420 million)

  2. The UK had about ~50 million internet users in 2010.

  3. ~360 million

  4. there is no indication of each countries population to compare the frequencies and the variablility of the population size with.

9

  1. ~69%

  2. ~52.8 million

  3. interential because the data is based on a sample of a population, not on the entirety of the US adult population in 2010.

11

  1. ~42% of Americans aged 18-34 are more likely to buy products made in the USA. ~61% of Americans aged 35-44 are more likely to buy products made in the USA.

  2. ~75% of Americans aged 55+ are more likely to buy products made in the USA, which is the highest proportion for this category out of all the age groups.

  3. Respondents in the age group 18-34 are less likely to buy products made in the USA.

  4. The older you are, the more likely you are to buy products made in America

13

Never: 0.026

Rarely: 0.068

Sometimes: 0.116

Most of the time: 0.263

Always: 0.527

  1. 52.7%

  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 because it uses a set of numbers to describe the given data

15

More then 1 hour: 0.368

Up to 1 hour: 0.187

A few time a week: 0.129

A few times a month: 0.079

Never: 0.237

  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. The number is based on a sample size, not the entire adult population of the united states.

2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. left skewed data

10

  1. The most frequent number of cars sold during a week is 1 and 2. The most number of cars sold in one week is 12.

  2. 2

  3. 3.8%

  4. right skewed data

11

  1. 200

  2. 10

  3. class: 60-70 frequency: 2. class: 70-80 frenquency: 3. class: 80-90 frequency: 13. class: 90-100 frequency: 42. class: 100-110 frequency: 58. class: 110-120 frequency: 40. class: 120-130 frequency 31. class: 130-140 frequency: 8. class: 140-150 frequency: 2. class: 150-160 frequency: 1.

  4. 100-110

  5. 150-160

  6. 5.5%

  7. no

12

  1. 200

  2. Skip this problem

  3. 0-200 (26)

  4. right skewed

  5. It doesn’t account for the population of each state and if the accidents happened mostly in cities, on highways, or in more rural/suburban areas.

13

  1. bell shaped because most people are middle class, then some people are lower class and a few people are upper class.

  2. bell shaped because most students will do pretty well, which will become the average, then fewer students will do really well or pretty poorly.

  3. skewed right because there is a limit to the number of people that can fit in a house.

  4. skewed left because someone is more likely to get alzheimers as they get older.

14

  1. This will probably vary by what data you’re looking at. If you look at a group of recovering alcoholics, the graph will probably be left skewed because they are trying to stay away from drinking. If you look at another group, the graph would probably be uniform because the amount those groups drink each week may be more consistant.

  2. left skewed because the students age a year as they advance in academic year ranking

  3. skewed left because someone is more likely to have a hearing aid as they get holder and become more hard of hearing

  4. bell shaped because most men will be at an average of say 6ft, but there will be people who are taller than 6ft and people who are shorter than 6ft.