7
China
50 million
350 million
relative frequency would be more accurate
9
69%
55.2 million
inferential- only 8% of the population was survery
11
18-34: 42% 35-44: 61%
55+ year olds
18-35
The older you are the more likely you are to buy when a product is made in america
13
Never: 2%
Rarely: 6%
Sometimes: 11%
Most of the time: 26%
Always: 52%
52%
9%
d e f
my_data <- c(125, 324, 552, 1257, 2518)
groups <- c("Never", "Rarely", "Sometimes", "Most", "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")
15
More then 1 hour: 36%
Up to 1 hour: 18%
A few time a week: 12%
A few times a month: 7%
Never: 23%
c d e
my_data <- c(377, 192, 132, 81, 243)
groups <- c("More 1", "Up to 1", "Few times week", "Few times month", "Never")
barplot(my_data, main = "Use the internet", names.arg = groups)
barplot(my_data, main = "Use the internet", names.arg = groups, col = c("red","blue","green","yellow", "black"))
rel_freq <- my_data / sum(my_data)
barplot(rel_freq, main = "Use the internet", names.arg = groups, col = c("red","blue","green","yellow","black"))
pie(my_data, labels = groups, main = "Use the internet")
9
8
2
15
4
15%
bell shaped
10
4
9
17.3%
right skew
11
200
10
60- 1% 70- 1.5% 80- 6.5% 90- 21% 100- 29% 110- 20% 130- 15.5% 140- 1% 150- .05% 160- 0
100-110
150-160
5.5%
no
12
200
Skip this problem
0-200
right skew
data doesnt say states
13
Left: more poor than rich
bell: most students are average
right: families usually live in houses
left: older people get alzheimers
14
right: most people dont drink heavily
bell: each grade has equal numbers of students
left: old people need hearing aids
bell: men are all different heights, mostly average height