Section 2.1

7

  1. OF

  2. 15

  3. 15

  4. These positions should be recorded as MVPs, instead of treating the three positions as one single position.

9

  1. 69%

  2. 55.2 million

  3. Inferential

11

  1. 0.42; 0.61

  2. 55+

  3. 18-34

  4. As age inscreased, so does the likelihood to buy American.

13

  1. Response
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. This is a descriptive statement because it’s recording a result of the sample.

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. 0.2371, or about 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. No level of confidence is provided along with the estimate.

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. Bell shaped

10

  1. 4

  2. 2

  3. 2%

  4. Right-Skewed Distribution

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. 7

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

  3. 0-200

  4. Right-skewed distribution

  5. This data is not 100% accurate because we do not have all of the information; we are missing exactly how these deaths occured. Also, the “safety” of the roads does not correlate with the number of people who drink and drive in each state. A more fair comparison could be made by getting reports from police stations in Vermont and Texas and seeing how many reports of DUIs/ drunk driving are in each state.

13

  1. Most likely skewed right. Most household incomes would be on the left, with less higher incomes on the right.

  2. Most likely bell shaped. Most scores would be near the middle range, with scores tapering off equally in either direction.

  3. Most likely skewed right. Most households would have around 1-4 people, with fewer houselds holding a higher number of people.

  4. Most likely skewed left. Most Alzheimer’s patients will fall in the older-aged categories and there would be less younger-aged patients.

14

  1. Most likely left skewed if the first section of the historgram started on a Monday, becuase people tend to drink more on the weekends than during weekdays.

  2. Most likely left-skewed, because kids get older as their public school education continues in their public school district.

  3. Most likely left skewed, because as people get older they tend to require hearing aids more so than younger people.

  4. Most likely uniform, because height doesn’t really change with age and full grown males have an average height.