2.1 Assess Your Understanding

  1. China had the most Internet users in 2010

  2. The United Kingdom had approx. 50 million Internet users in 2010

  3. China had approx. 345 million more internet users than Germany in 2010

  4. China’s larger population affects it’s frequency, making the data unfair in how it’s being compared to to other countries

  1. Approx. 69% of the respondents believe divorce is morally acceptable

  2. Approx 60 million adult Americans believed that divorce is morally wrong

  3. I would say this statement is descriptive because it helps us draw conclusions regarding the hypotheses formed

  1. 18-34 year old respondents more likely to buy when made in America = approx. 45%, 35-44 year old respondents more likely to buy when made America = approx. 61%

  2. The 55+ age group has the greatest proportion who are more likely to buy when made in America

  3. The 18-34 year old age group ahs a majority of respondents who are less likely to buy when made in America

  4. As the age group grows, the likelihood to buy when made in America grows as well

  1. Response—–RF

    Never—–0.0262

    Rarely—–0.0678

    Sometimes—–0.1156

    Most of the time—–0.2632

    Always—–0.5272

  2. 52.7% of respondents answered “Always”

  3. Approx. 3% of respondents answered “Never,” approx. 7% of respondents answered “Rarely”

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

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

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

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

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

rel_freq <- my_data / sum (my_data)

barplot(rel_freq, main = "College Survey", names.arg = groups)

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

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

pie(my_data, labels = groups, main = "College Survey")

  1. This is a descriptive statement

  1. Response—–RF

    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—–0.079

    Never—–0.237

  2. 243/1025 never use the internet

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

groups <- c("More than 1 hr", "Up to 1 hr", "Times/week", "Times/month-", "Never")

barplot(my_data, main = "Use the Internet?", names.arg = groups)

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

groups <- c("More than 1 hr", "Up to 1 hr", "Times/week", "Times/month-", "Never")

rel_freq <- my_data / sum (my_data)

barplot(rel_freq, main = "College Survey", names.arg = groups)

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

groups <- c("More than 1 hr", "Up to 1 hr", "Times/week", "Times/month-", "Never")

pie(my_data, labels = groups, main = "College Survey")

  1. The statement is not backed up with any other pieces of information to support this data

2.2 Assess Your Understanding

  1. 8 was the most frequent outcome of the experiment

  2. 2 was the least frequent outcome of the experiment

  3. We observed 7 fifteen times

  4. There were four more 5’s observed than 4’s

  5. 15%

  6. The shape of the distribution is bell curve

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

  2. For 9 weeks, two cars were sold

  3. 69.2%

  4. The shape of the distribution is skewed right

  1. 200 students were sampled

  2. Class width = 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. Class 100-109 has the highest frequency

  5. Class 150-159 has the lowest frequency

  6. 5.5% of students had an IQ of at least 130

  7. None of the students have an IQ of 165

  1. Class width = 4 (rounded up)

  2. 0-199, 200-399, 400-599, 600-799, 800-999, 1000-1199, 1200-1399, 1400-1599

  3. Class 0-199 has the highest frequency

  4. The shape of the distribution is skewed right

  5. The amount of deaths does not provide information to draw the conclusion that the roads in Vermont are safer. Alcohol related accidents have more to do with the probability of people being heavier drinkers in one place more than another.

  1. Skewed right because more people are less likely to earn large sums of money, especially now a days when we are recovering from a recession and there is a significant amount of people who are unemployed

  2. Bell curve because there is an average score that more people are likely to get on a standardized test on the SAT (1500)

  3. Bell curve because there is a number of children more people/households are likely to have

  4. Skewed right because patients diagnosed with Alzheimer’s disease are usually old

  1. Skewed left because people generally consume alcohol towards the end of a week

  2. Bell curve because people attend school usually all of their life between the ages of 5 and 18

  3. Skewed left because people that need hearing aids are usually in their later years

  4. Bell curve because men’s there is an average height most full-grown men reach

Histogram of Sepal.Length from iris data set:

names(iris)

hist(iris$Sepal.Length)

Description of shape: The shape of distribution is a bell curve because the highest frequency is in the middle and the frequencies decrease to the left and to the right of the middle.