1. China had the most internet users in 2010.

  1. The UK had approximately 50 million internet users in 2010.

  2. There were approximately 350 million more internet users in China than in Germany in 2010.

  3. This graph may be misleading because it doesn’t take into account the population of each country. If it was designed to be more accurate, it would be internet users per capita.

    1. 69% of respondants believe divorce is morally acceptable.

  1. 55.2 million adults would believe that divorce is morally wrong.

  2. This statement would be inferential because descriptive statistics does not allow us to draw conclusions from the data analyzed. Also, inferential statistics allows us to use samples to make generalizations about populations that created the samples.

    1. The proportion of 18-34 year olds that are more likely to buy a product made in America is .45 out of 1. The proportion of 35-44 year olds that are more likely to buy a product made in America is .61 out of 1.

  1. The group who is 55 years old and up has the greatest proportion of likliness to buy a product made in America.

  2. The 18-34 year olds have the greatest proportion of who is less likely to buy a product made in America.

  3. The association between age and likliness to buy when made in America is the higher the age, the more likely customers are to buy a product made in America.

  1. a, d-f
my_data <- c(125, 324, 552, 1257, 2518)

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

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

rel_freq <- my_data / sum(my_data)

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

pie(my_data, labels = groups, main = "Passenger Seatbelts")

b.The percentage of students who answered ‘always’ is 53%.

  1. The percentage of students who answered ‘never’ or ‘rarely’ is 9%.

  2. This is an inferential statement because it is giving a conclusion about the data analyzed.

  1. 24% of participants never use the internet.

c-e.

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

groups <- c("1+ hrs/day", "1 hr/day", "weekly", "monthly", "never")

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

rel_freq <- my_data / sum(my_data)

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

pie(my_data, labels = groups, main = "Internet Usage")

  1. The local news broadcast applied the information from the sample to the entire population, which is incorrect.
    1. The most frequent outcome of the experiment was rolling an 8.

  1. The least frequent outcome of the experiment was rolling a 2.

  2. We observed a 7 15 out of 100 times.

  3. We observed approximately 4 more 5’s than 4’s.

  4. The percentage of 7’s rolled ws 15%.

  5. The shape of the distribution is symmetric (or a bell).

    1. The most frequent number of cars sold in a week is 4.

  1. Two cars were sold for 9 weeks.

  2. The percentage of time two cars were sold is 17%.

  3. The shape of the distribution is symmetric (or a bell).

    1. There were 200 students total that were sampled.

  1. The class width is 60-160.

  2. 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

  1. The class with the highest frequency is 100-109.

  2. The class with the lowest frequency is 150-159.

  3. The percent of students with an IQ of at least 130 is approximately 6%.

  4. No students had an IQ of 165.

    1. The class width is 0-1600.
  1. 0-199

200-399

400- 599

600-799

800-999

1000-1199

1200-1399

1400-1599

  1. The class with the highest frequency is 0-199

  2. The shape of the distribution is right skewed.

  3. This statement is wrong because it doesn’t take into consideration that Texas is not only bigger than Vermont in size, but also population. This could be fixed by adjusting the data to be a ratio of the population.

    1. This would be symmetric because most people receive an average income and less people are poor or extremely rich.

  1. This would be symmetric because most people get an average score and fewer get a high score or a low score.

  2. This would be uniform because there is an equal chance that 5 people could live in a household as there is that there will only be 2 people.

  3. Left skewed because older patients are more likely to have this disease.

    1. Left skewed because there is eventually a limit on how many drinks it is possible to have in a certain period of time.

  1. Uniform because there can be an equal amount of students that are the same age and also different ages.

  2. Left skewed because there is a higher chance of older patients requiring hearing aids as compared to younger patients.

  3. Right skewed because there is a biological limit on how tall/short men can be.

  1. 24% of households have two children under the age of 5.

  2. 60% of households have one or two children under the age of 5.

  1. The basketball player misses on her fourth free throw 14% of the time.

  2. She misses on her tenth free throw 2% of the time.

  3. She made at least 5 free throws 14% of the time.

hist(iris$Sepal.Length)

The shape of this histogram is symmetric. This is because the data is almost evenly distributed between the two sides of the graph.