1. China
  2. 50 million
  3. 350 million
  4. This graph may be misleading because the y axis describes the frequency of internet users, not necessarily the number of internet users in total
  1. Approximately 69%
  2. Approximately 55,200,000
  3. I would say this statement is inferential, because they are assuming that because the 8% responded with “Depends on the situation”, that implies that they believe that “divorce is acceptable in certain situations”
  1. 44:100 (22:50 or 44%) of 18-34 year olds are more likely to buy 61:100 (61%) of 35-44 year olds are more likely to buy
  2. 55+
  3. 18-34
  4. The apparent association is that as age increases, so does the likelihood to buy when made in America
  1. Relative frequency (total=4776) Never 125 125/4776=.026 Rarely 324 324/4776=.068 Sometimes 552 522/4776=.116 Most of the time 1257 1257/4776=.263 Always 2518 2518/4776=.527
  2. About 52.7% answered always
  3. About 9.4% answered Never or Rarely
my_data <- c(125, 324, 552, 1257, 2518)

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

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

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

rel_freq <- my_data / sum(my_data)

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

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

g. This is an inferential statement because it cannot be proven from this data that 52.7% of college students always wear their seatbelt. The only thing that can be proven is that 52.7% say that they always wear their seatbelt.

  1. Relative Frequency (total=1025) More than 1 hour a day 377 377/1025=.368 Up to 1 hour a day 192 192/1025=.187 A few times a week 132 132/1025=.129 A few times a month or less 81 81/1025=.079 Never 243 243/1025=.237
my_data <- c(377, 192, 132, 81, 243)

groups <- c("1hr+", "up to 1hr", "few Xweek", "few Xmonth", "never")

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

barplot(my_data, main = "Time Spent on Internet", names.arg = groups, col = c("red","blue","green","yellow", "black"))

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Time Spent on Internet", names.arg = groups, col = c("red","blue","green","yellow", "black"))

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

  1. The local news broadcast failed to mention that these statistics were from a small sample of Americans and cannot be generalized to encompass the entire country.
  1. The most frequent outcome was the 8 value of dice
  2. The least frequent outcome was the 2 value of dice
  3. The 7 was observed 15 times
  4. 4 more 5’s were observed than 4’s
  5. A 7 was observed 15% of the time
  6. The shape of the distribution is slightly left skewed
  1. The most frequent number of cars solid in a week is 4
  2. For 9 weeks there were 2 cars sold
  3. 2 cars were sold 17.3% of the time
  4. The distribution is slightly right skewed
  1. 200 students were sampled
  2. The class width is 10
  3. 60-70 2 70-80 3 80-90 13 90-100 42 100-110 58 110-120 40 120-130 31 130-140 8 140-150 2 150-160 1
  4. 100-110 has the highest frequency
  5. 150-160 has the lowest frequency
  6. 5.5% of students had an IQ of at least 130
  7. None of the students shown have an IQ of 165
  1. The class width is 200
  2. 0-200 fatalities 200-400 fatalities 400-600 fatalities 600-800 fatalities 800-1000 fatalities 1000-1200 fatalities 1200-1400 fatalities 1400- 1600 fatalities
  3. 0-200 had the highest frequency
  4. The distribution is right skewed
  5. The statement is wrong because just because there are less alcohol related deaths doesn’t necessarily mean that the roads are safer. There could be more people who were injured/died due to other causes on the road as well.
  1. Household incomes: Right skewed because there are less and less people making the most money in the US annually
  2. Standardized test scores: Bell shaped because most students will score in the middle, but fewer students score especially high and especially low
  3. Number of people living in a household: Right skewed because more people will live alone or with a small number of people versus a large group
  4. Ages of patients diagnosed with Alzheimer’s: Left Skewed because as people get older they are more likely to get Alzheimer’s
  1. Number of alcoholic drinks consumed per week: Right skewed because more people are likely to have a few drinks a week than many drinks a week
  2. Ages of students in a public school district: Right skewed because as students get older they are les likely to attend a public school
  3. Ages of Hearing aid patients: left skewed because at adults get older they are more likely to need a hearing aid
  4. Heights of full grown men: Bell shaped because most men are average height and less men are either below average or above average
hist(iris$Sepal.Length)

The graph is bell shaped