Section 2.1

9

  1. 69

  2. 55.2 million

  3. Inferential

11

  1. .42

  2. .60

  3. 18-34

  4. As age increases, likelihood to buy made in America increases

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. 52.7%

  2. 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. Descriptive

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. 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 curve

10

  1. 4

  2. 9

  3. 9%

  4. Right skewed

11

  1. 200

  2. 10

  3. 60-69, 2; 70-79, 3; 80-81, 13; 90-91, 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. No

12

  1. 200

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

  3. 0-199

  4. Right skewed

  5. It isn’t necesiarly the roads that are unsafe. The data could represent that people in Texas drink and drive more often than people in Vermont drink and drive.

13

  1. Right skewed– more people earn below the average income than above the average income.

  2. Bell shaped– Most scores will be in the middle

  3. Right skewed– Most households have more people

  4. Left skewed– most patients will be older, with fewer patients being younger.

14

  1. Left skewed– most people will have a couple drinks per week, while a few will drink more.

  2. Uniform– there are usually an equal number of students at all ages

  3. Right skewed– most people will be older, with a few people being young

  4. Bell shaped – most people will be in the center, but some will be both shorter or taller.

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%