Section 2.1

9

  1. 69%.

  2. 55.2 million.

  3. inferential because we are making a claim assuming that the sample represents the population.

11

  1. Proportion more likely to buy of 18 to 34 is 42% and proportion more likely of 35 to 44 is 61% .

  2. 55+.

  3. 18 to 34.

  4. Older people are more likely to buy products made in the US.

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% answered rarely.

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.

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% never use the internet.

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

  6. bell curve.

10

  3. 17.3%.

  4. bell curved.

11

  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

  2. 0-199,200-599,600-799,800-999,1200-1399.

  3. 0-199.

  4. left skewed.

  5. drunk driving is the discrete variable and the traffic fatalities is the continuous variable so calling the road “safer” is not what was being determined. The data shows that because Texas had 1296 alcohol-related death than Vermont with 23 means that there are less drunk drivers.

13

  1. Right skewed because the majority of people are clustered to the left there are a few that are towards the right.

  2. bell-shaped because the mean is the mode, but there are people who are score less than and a few who are higher than.

  3. Right skewed because most families are smaller, but there are some that are larger.

  4. Left skewed because people most people are diagnosed when they are older, however some people can be diagnosed when they are younger.

14

  1. Right skewed because most people drink moderately, but some people drink more

  2. Uniformed because you are going to have the same number of students in each grade.

  3. Left skewed, older people need hearing-aid more often than younger people.

  4. bell-shaped because when guys are growing up they are shorter than the average height and after a certain age people start to shrink.

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%.