2.1 7- a-China b-50 million c-Around 350 million d- Because China’s population is much bigger than the other countries and it can influence a higher frequency, it should have been used Relative Frequency instead of Frequency, so it would be shown the proportion of observation that belong in every cathegory,

9- a-69% b-55,2 million (If 100% is 240 million, 23% would be 55,2 million) c-Inferential, because the information is not stated as an exact number, but as a percentage.

11-a-0.43 and 0.61 b-55+ c-18-34 d-As people get older, it is more likely to buy in America.

13-a- Response and Relative Frequency: Never-0.0262 Rarely-0.0678 Sometimes-0,1156 Most of the time- 0,2632 Always-0,5272 b-52.7% c-9.4%

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

groups <- c("never", "rarely", "sometimes", "mostly", "always")

barplot(my_data, main = "13", names.arg = groups)

barplot(my_data, main = "13", names.arg = groups, col = c("red","blue","green","yellow"))

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "13", names.arg = groups, col = c("red","blue","green","yellow"))

pie(my_data, labels = groups, main = "13")

g-It is an inferential statement because it takes the result of a sample and generalize to the whole population.

15-

a- Response and Relative Frequency: More than 1 hour- 0,3678 Up to 1 hour- 0,1873 A few times a week- 0,1288 A few times a month- 0,079 Never- 0,2371

b-o,2371 which is around 23.7%

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

groups <- c("1 hour+", "1 hour", "weekly", "monthly", "never")

barplot(my_data, main = "15", names.arg = groups)

barplot(my_data, main = "15", names.arg = groups, col = c("red","blue","green","yellow"))

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "15", names.arg = groups, col = c("red","blue","green","yellow"))

pie(my_data, labels = groups, main = "15")

f-The wrong thing about this statement is that the results are based on a sample, so there is no certain about the probability, and it can’t be generalized.

2.2

9- a-8 b-2 c-15 d-4 e-15% f-bell-shaped

10- a-4 b-9 c-9% d-bell-shaped

11- a-200 b-10 c-60-69=2; 70-79=3; 80-89=13; 90-99=42; 100-109=58; 110-119=40; 120-129=31; 130-149=2; 150-159=1 d-100-109 e-150-159 f-5,5% g-no

12- a-8 b-0-199; 200-399; 400-599; 600-799; 800-999; 1000-1999; 1200-1399; 1400;1599 c-0-199 d-skewed-right e- What is wrong is that the reporter is just comparing the frequencies. However, the population in Texas can be much bigger than in Vermont, and it can influence the results. So a right way to determine that would be using relative frequency instead of frequency, to be more specific.

13- a-skewed-right, because most of the incomes would be lower(to the left), and the ones that are very high would be much fewer. b-bell-shaped, beause most of the results will be in the middle, and a few will be very good or very bad( right and left) c-skewed right, because most of the house holds have a common number of habitants(1-5), and fewer will have more people living with them d-skewed-left, because the disease attack mostly older people(left), and very few young people(right).

14- a-bell-shaped, because usually, people have a drink 4,5 times a week(even a beer), so it would be higher in the middle, but few people drink a lot(10+) or very little. b-skewed-right, I would say that because usually parents try to move where there are public school for theirs kids, and as they get older they leave school(17-18 or older- college). c-skewed-left, because usually people who have hearing problem are older people(left) d-bell-shaped, because most of times they are located in the middle(170-190 cm), however there are very few old men that are really short or really tall.

hist(iris$Sepal.Length)

I would say this is uniform because the concentration in the middle is almost the same, and the high ends look the same.