Section 2.1

9

  1. nearly 70%

  2. 23% x 10,000,000 = 2.6 million

  3. inferential - it’s using data drawn from the population to make an inference about the population

11

  1. 18-34 = .42 35-44 = .61

  2. 55+

  3. 18-34

  4. the odler you are, the more likely you are to buy “made in america”

13

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

rel.freqq <- datt/sum(datt)

categoriess <- c("Never", "Rarely", "Sometimes", "Most of time", "Always")


answerr <- data.frame(categoriess,rel.freqq)

answerr
##    categoriess  rel.freqq
## 1        Never 0.02617253
## 2       Rarely 0.06783920
## 3    Sometimes 0.11557789
## 4 Most of time 0.26319095
## 5       Always 0.52721943

  1. about 52.7%

  2. about 9.4%

barplot(datt,main="Seat Belt Usage",names=categoriess, col =c("red","blue","green","yellow","orange"))

barplot(rel.freqq,main="Seat Belt Usage",names=categoriess, col =c("red","blue","green","yellow","orange"))

pie(datt,main="Seat Belt Usage",labels=categoriess, col =c("red","blue","green","yellow","orange"))

  1. this is a descriptive statement because it is the description of the sample surveyed.

15

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

rel.freq <- dat/sum(dat)

categories <- c("More 1", "Up to 1", "Few a week", "Few a month", "Never")


answer <- data.frame(categories,rel.freq)

answer
##    categories   rel.freq
## 1      More 1 0.36780488
## 2     Up to 1 0.18731707
## 3  Few a week 0.12878049
## 4 Few a month 0.07902439
## 5       Never 0.23707317

  1. about 23.7%

barplot(dat,main="Internet Usage",names=categories, col =c("red","blue","green","yellow","orange"))

barplot(rel.freq,main="Internet Usage(Relative Freq)",names=categories, col =c("red","blue","green","yellow","orange"))

pie(dat,main="Internet Usage",labels=categories, col =c("red","blue","green","yellow","orange"))

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. bell shaped normal distribution

10

  1. 4 cars

  2. 9 weeks

  3. about 28.8%

  4. relatively normal distribution - bell shaped

11

  1. 200 students

  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. none in this sample, no.

12

  1. 200

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

  3. 0-1999

  4. skewed right

  5. less alcohol-related incidents do not necessairly mean “safer roads”, as there are many factors that go into how safe a road is to drive on. in the terms of the study, it is fair that there are less alcohol related deaths on the roads in Vermont than there are in Texas, but not necessairly accidents in whole.

13

  1. slightly right-skewed, many americans are “middle-class”, though it is argued that the middle class is disappearing so this curve probably changes often and rapidly

  2. bell-shaped - most american teenagers probably fall into the mid twenty shape, with many above and many below that.

  3. right skewed - no less than 1 can live in a house hold, and the “typical” family is generlly considered to be 2-4 people, so it is safe to say most would be concentrated in the 1-4 range with lower numbers going higher than that.

  4. left skewed - most people develop memory-loss later in life with old age. (alzheimer’s can only be certainly developed after death)

14

  1. right skewed - you can’t have less than 0 drinks a week and not everyone drinks, so most people will be in the lower numbers with the heavier drinkers being less.

  2. relatively uniform (maybe right skewed depending on drop-out numbers and ages being studied), but Americans must go to school until they’re 16

  3. left skewed - many people suffer from hearing loss later in life

  4. bell shaped - there is an average height for american men, and some will be taller and some will be shorter.

15

dattt <- c(16, 18, 12, 3, 1)

rel.freqqq <- dattt/sum(dattt)

categoriesss <- c("Zero", "One", "Two", "Three", "Four")

answerrr <- data.frame(categoriesss,rel.freqqq)

answerrr
##   categoriesss rel.freqqq
## 1         Zero       0.32
## 2          One       0.36
## 3          Two       0.24
## 4        Three       0.06
## 5         Four       0.02

  1. 24%

  2. 60%