Section 2.1

9

  1. 69%

  2. 240,000,000＊0.22＝52,800,000

  3. The statement is inferential because Gallup can’t ask every single one of adult Americans. Based on random sample Gallup surveyed, he gives an inferential statement of every single one of adult Americans.

11

  1. 43%, 61%

  2. 55+

  3. 18-34

  4. The older you are, the more likely you will buy when 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. 0.527

  2. 0.0939

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. It is an inferential statement.

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. 0.237

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. 11-7=4

  5. 0.15

  6. bell shaped

10

  1. 4

  2. 9

  3. 17.3%

  4. skewed right

11

  1. 200

  2. 10

  3. 60-69 0.01 70-79 0.015 80-89 0.065 90-99 0.21 100-109 0.29 110-119 0.2 120-129 0.155 130-139 0.04 140-149 0.01 150-159 0.005

  4. 100-109

e)150-159

  1. 0.055

  2. no

12

  1. 200

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

  3. 0-199

  4. skewed right

  5. should use relative frequencies

13

  1. skewed right since income cannot be negative, and very few people have infinite incomes.

  2. bell shaped, since most people do ok and it’s hard to do very well or very poor.

  3. skewed right, since no fewer than one person can live in a household, and it’s also hard to say a upper limit of how many people living in one household.

  4. skewed left, since most people are diagnosed before death, not after.

14

  1. skewed right, since many people do not draink, and many people who drink don’t drink heavily.

  2. uniform distribution, since students are placed in school according to their ages.

  3. skewed left, since there are more old people need hearing aid. Though there is upper limit on age.

  4. bell shaped, since most people will be within a middling height range, while very few are extremly tall or short.

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. 0.24

  2. 0.60