HW2_skeleton

Section 2.1

9

1. 69%

2. 55.2 million

3. inferential because it’s making a conclusion based on observed data

11

1. .42; .61

2. 55+

3. 18-34

4. the older you are the more likely you are to buy products made in America

13

1. Never: .02617, Rarely: .06783, Sometimes: .11558, Most of the time: .26319, Always: .52721

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

2. 52.721%

3. 9.4%

4. barplot(datt,main=“Seat Belt Usage”,names=categoriess, col =c(“red”,“blue”,“green”,“yellow”,“orange”))

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

5. barplot(rel.freqq,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"))

6. pie(datt,main=“Seat Belt Usage”,labels=categoriess, col =c(“red”,“blue”,“green”,“yellow”,“orange”))

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

7. inferential because it’s a generalization

15

1. More than 1: .36780, Up to 1: .18732, A few times a week: .12878, A few times a month: .07902, Never: .23707

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

2. 24%

3. barplot(dat,main=“Internet Usage”,names=categories, col =c(“red”,“blue”,“green”,“yellow”,“orange”))

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

4. barplot(rel.freq,main=“Internet Usage(Relative Freq)”,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"))

5. pie(dat,main=“Internet Usage”,labels=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-shape

10

1. 4

2. 9

3. 17%

4. skewed right

11

1. 200

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. no

12

1. 200

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

3. 0-199

4. skewed right

5. There are too many lurking variables and also Texas is much larger (and has more people) than Vermont.

13

1. skewed right - lower and middle classes are larger than the higher class

2. bell-shaped - students will evenly fall below or above the average

3. skewed right - people are single and live with smaller number of people more often

4. skewed left - typically older people affected

14

1. skewed right - most people don’t drink too much

2. uniform - classes are typically similar in size

3. skewed left - typically older people affected

4. bell-shape - men will evenly fall below or above the average

15

1. 0: .32, 1: .36, 2: .24, 3: .06, 4: .02

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

2. 24%

3. 60%