Reading 2.1; #7-15 odds

7

  1. China
  2. 50 million users
  3. 350 million users
  4. It would have been better to use relative frequency because China’s population is much larger compared to the other countries.

9

  1. 69%
  2. 55.2 million
  3. Inferential, because this data is based on a sample.

11

  1. 0.42; 0.61
  2. 55+
  3. 18-34
  4. As age increases, so does likelihood to buy American.

13

  1. Relative frequency distribution:

Never: 0.0262

Rarely: 0.0678

Sometimes: 0.1156

Most of the time: 0.2632

  1. 52.7%
  2. 9.4%
my_data <- c (125, 324, 552, 1257, 2518)

groups <- c ("never", "rarely", "sometimes", "most of the time", "always")

barplot(my_data, main = "How Often College Students Wear Seat Belts", names.arg = groups)

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

groups <- c ("never", "rarely", "sometimes", "most of the time", "always")

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "How Often College Students Wear Seat Belts", names.arg = groups)

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

groups <- c ("never", "rarely", "sometimes", "most of the time", "always")

pie(my_data, labels = groups, main = "How Often College Students Wear Seat Belts")

  1. This is an inferential statement because it is taking in data from a sample.

15

  1. Relative frequency distribution:

More than 1 hour a day: 0.3678

Up to 1 hour a day: 0.1873

A few times a week 0.1288

A few times a month or less 0.0790

Never 0.2371

  1. 0.2371

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 = "Time Spent on the Internet", names.arg = groups)

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")

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Time Spent on the Internet", names.arg = groups)

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")

pie(my_data, labels = groups, main = "Time Spent on the Internet")

  1. No level of confidence is provided along with the estimate.

Reading 2.2; #9-14

9

  1. 8
  2. 2
  3. 15
  4. 4
  5. 15%
  6. Bell shaped

10

  1. 4
  2. 9
  3. 17%
  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. 0-199, 200-399, 400-600, 1000-1200, 1400-1600
  3. 0-199
  4. Skewed right
  5. It does not take into account that Texas’ population is much larger than Vermont’s population.

13

  1. Skewed right. Most household incomes will be to the left with fewer higher incomes to the right.
  2. Bell-shaped. Most scores will occur near the middle range, with scores tapering off equally in both directions.
  3. Skewed right. Most households will have, say, 1 to 4 occupants, with fewer households having a higher number of occupants.
  4. Skewed left. Most Alzheimer’s patients will fall in older-aged categories, with fewer patients being younger.

14

  1. Skewed right. There are only so many drinks a person can consume.
  2. Uniform. There should be about the same number of students in each grade and thus age groups.
  3. Skewed left. Older people are far more likely to have hearing aids.
  4. Bell shaped. Most heights will fall somewhere in the middle with some extreme lows (very short men) and highs (very tall men).

Additional problem:

hist(iris$Sepal.Length)

This histogram is bell-shaped and is not skewed, but not uniformed either.