Section 2.1

7

  1. Outfield had the most MVPS, because it has the highest bar.

  2. 15

  4. The graph makes it look like one position, when in reality it is 3.

9

  1. 69%

  2. 55.2 Million

  3. Inferential because it is a generalization based on observed data.

11

  1. .42 for 18-34 and .61 for 34-44.

  2. The 55+ age group.

  3. the 18-34 group.

  4. As age increases, so does the likelihood to buy a product 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. 52.72%

  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. Inferrential because it infers something about the entire population based on a sample survey.

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. .2371

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"))

  1. The statement provides an estimate but no level of confidence is given.

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. Bell-Shaped

10

  1. 4 cars

  2. 9 weeks

  3. 17.3%

  4. skewed right

11

  1. 200

  2. 10

  3. 60-69,2; 70-79,3; 80-89,13;. . . . . . 150-159,1.

  4. 100-109

  5. 150-159

  6. 5.5%

  7. No.

12

  1. 200

  2. 0-199,. . . . . . 1200-1399

  3. 0-199

  4. Right Skewed

  5. Texas has much larger population then Vermont, so they should report % of population.

13

  1. Likely skewed right.

  2. Likely bell shaped.

  3. Likely skewed right.

  4. Likely skewed left.

14

  1. Likely skewed right.

  2. Likely uniform.

  3. Likely skewed left.

  4. Likely bell shaped.