HW2_skeleton

Section 2.1

9

1. 69%

2. 55,200,000

3. inferential

11

1. .43 proportio , .61 proportion

2. 55+

3. 18-34

4. as age increases, so does the likelihood to buy products 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

2. 52.721943%

3. 9.4011%

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"))

7. 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

2. 243/1025

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. 5

5. .15%

6. bell-shaped

10

1. 4

2. 9

3. 17.30769%

4. skewed right

11

1. 200

2. .5

3. Class are 60-69, 70-79,80-89,90-99, 100-109, 110-119, 120-129, 130-139, 140-149, 150-160 with frequencies 2, 3, 13, 42, 58, 40, 31, 8, 2, 1

4. 100-109

5. 150-160

6. 5.5%

7. no

12

1. 200

2. Class are 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 the data is connecting alcohol consumption and driving to traffic fatalities. The road safety was not something included in this data, and thus it is jumping to conclusions.

13

1. skewed right because most household incomes will be in the left with a few higher incomes to the right

2. bell-shaped because most scores occur in the middle range with scores tail off at both ends

3. skewed right because fewer households have higher number of occupants so the tail to the right of the peak is longer than the tail to the left of the peak

4. Skewed left because alzheimers disease falls in the old aged category . So the tail to the left of the peak is longer than the tail to the right of the peak.

14

1. bell shaped because because most people would fall in the middle with a few who either drink more or less

2. uniform because most grades have the same number of students

3. skewed left because most hearing-aid patients are older

4. bell-shaped because most men are around the same height with a few falling above or below average

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

2. 24%

3. 60%