Section 2.1 7-15 odd
7. Internet Users
a. China
b. 50 million users
c. About 350 million users
d. Because it is not a relative frequency graph and so some of the countries will not have the same number of people that live there, which will make the data hard to compare.

  1. Divorce
  1. About 69%
  2. 55,200,000 Adults
  3. Inferential, because based off the graph you can infer that the column is at about 8% but you can not be sure with out a actual number describing the column.
  1. Made in America
  1. 18-34: About 0.43
    35-44: About 0.61
  2. 55+
  3. 18-34
  4. As age increases, the likelihood to buy american-made products increases.
  1. College Survey
  1. Relative Frequency Distribution
    Never: 0.0262
    Rarely: 0.0678
    Sometimes: 0.1156
    Most of the time: 0.2632
    Always: 0.5272
  2. 52.7%
  3. 9.4%
my_data <- c(125, 324, 552, 1257, 2518)

groups <- c("Never", "Rarely", "Sometimes", "Most the time", "Always")

barplot(my_data, main = "Frequency Bar Graph", names.arg = groups)

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Relative Frequency Bar Graph", names.arg = groups)

pie(my_data, labels = groups, main = "Pie Chart")

  1. Inferential, because the survey does not specify that all college students were surveyed.
  1. Use the Internet?
  1. Relative Frequency Distribution
    More than 1 hour a day: 0.368
    Up to 1 hour a day: 0.187
    A few times a week: 0.129
    A few times a month or less: 0.079
    Never: 0.237
  2. 23.7%
my_data <- c(377, 192, 132, 81, 243)

groups <- c("More than 1 hour a day", "Up to 1 hour a day", "A few times a week", "A few times a month or less", "Never")

barplot(my_data, main = "Frequency Bar Graph", names.arg = groups)

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Relative Frequency Bar Graph", names.arg = groups)

pie(my_data, labels = groups, main = "Pie Chart")

  1. It just states the percentage does not back it up with other info, such as the amount of people in the study, of the survey to make the data more reliable.

Section 2.2 9-14
9. Sum of Two Dice
a. 8
b. 2
c. 15
d. 4
e. 15%
f. Bell
10. Car Sales
a. 4
b. 9
c. 17.6%
d. right skew
11. IQ Scores
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
130-139:8
140-149:2
150-159:1
d. 100-109
e. 150-159
f. 5.5%
g. No
12. Alcohol Related Traffic Fatalities
a. 200
b.
0-199
200-399
400-599
600-799
800-999
1000-1199
1200-1399
1400-1599
c. 0-199
d. Right skew
e. You can’t assume the other is safer and it’s not relative frequencies so the two can’t be accurately compared. The graph does not specify the states, so you can go as far as saying 0-200 incidents are most frequent.
13.
a. Right skew, poor people will make up more of the population than rich. Less household will make a lot of money.
b. Bell shaped, most score will be in the middle with few on the extreme ends of the spectrum.
c. Right skew, most households will have less people living in them.
d. Left skew, Alzheimer’s disease occurs in older people.
14.
a. Left skew, most people that consume a lot of alcohol and alcohol is a booming industry.
b. Right skew, as age increases more students will move to private schools.
c. Left Skew, older people are more likely to have a hearing aid.
d. Bell shaped, most guys stop growing around the same age.

The Iris histogram is right skewed, because the highest frequency is more towards the left and the right side of the graph is low so it can’t be a bell shaped.

hist(iris$Sepal.Length, main = "Histogram")