Section 2.1

9

  1. 69%
  2. 55.2 million people
  3. Inferential

11

  1. .42; .61

  2. 55+ age group is more likely to buy it

  3. 18-34 age group

  4. As you get older you are more likely to buy goods that are 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. 52.7%

  2. 9.45

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. .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. 4 more

  5. 15%

  6. bell shaped

10

  1. 4

  2. 9

  3. 17.3%

  4. bell-shaped

11

  1. 200 students

  2. 100

  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. no

12

  1. 0-1400

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

  3. 0-199

  4. skewed-right

  5. This statement is wrong because it fails to consider the differences between road safety and drunk driving. A fair comparasion could be made by taking in consideration outside factors such as population to fairly compare the two states.

13

  1. bell-shaped because in your mid-aged years you are making the most money while when you are young and old that number is slightly lower

  2. bell-shaped because the score distrubution is pretty spread out, most people ending up in the middle.

  3. skewed right because you are more likely to have less people living in a house

  4. skewed left because as you get older you are more likely to be diagnosed with the disease

14

  1. skewed right because most people probably drink 1-2 drink as opposed to 13 drinks a week.

  2. uniform because the age doesn’t really change

  3. skewed left because you are more likely to be a hearing aid patient when you are older.

  4. bell-shaped because there is an average height that people usually fall into range.

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%