Section 2.1

7

  1. OF

  2. 15

  3. 15

  4. The MVPs of left field, center field, and right field should be reported separately.

9

  1. 69%

  2. 55.2 million

  3. Inferential, because this is based off of a sample and not off of all adult Americans.

11

  1. 0.42, 0.61

  2. 55+

  3. 18-34

  4. As the age increases, they are more likely to buy products advertising that they 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. 9.4%

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. Descriptive, because it is reporting a result of the students surveyed.

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. 0.24

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. There is no level of confidence with the estimate.

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. Bell shaped.

10

  1. 4

  2. 9

  3. 17.3%

  4. Bell shaped.

11

  1. 200

  2. 10

  3. 60-69 had 2, 70-79 had 3, 80-89 had 13, 90-99 had 42, 100-109 had 58, 110-119 had 40, 120-129 had 31, 130-139 had 8, 140-149 had 2, 150-159 had 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. Within the data it does not specify what state was in which class, it only tells us how many states are in each class. Also the graph is not specific enough to give an exact number of fatalities such as 1296 or 23 as the class width is 200. There is nothing specific you can say about the relationship between Texas and Vermont from this graph except that it is most likely that they are both withing the 0-199 class.

13

  1. Right skewed because most incomes will be to the left while a there will be a few to the far right for the people who are extremely wealthy.

  2. Bell shaped because most scores will be in the middle while there may be a few more on the left and right.

  3. Skewed right because most houses will only have a few people while there may be some with an unusually high number of people.

  4. Skewed left because most patients will be older with a few rare cases being younger.

14

  1. Right skewed because the lowest class will have a high frequency for individuals who are underage. The next few classes may be frequent but not as frequent as the first for women who may not be able to drink as much and people who don’t choose to drink as much. Then it will taper off as fewer people choose to drink more.

  2. Right skewed as most kids would probably start in public school but then students may start to transfer to private school as they get older, especially for college.

  3. Left skewed as most patients will be older with a few rare cases being younger.

  4. Bell shaped as most will be in the middle with a few being shorter and a few being taller.