Section 2.1

7

  1. Position OF has the most MVPs.

  2. 15 MVPs.

  3. 30-15=15, so there’re 15 more MVPs.

  4. The outfield positions should be divided into 3 different fields rather than count as one.

9

  1. 69%.

  2. 240 x 0.23=55.2 million.

  3. It is inferential because it’s base on the generalization of the population.

11

  1. 0.42 for proportion of 18-34, and 0.61 proportion of 35-44.

  2. The 55+ age group.

  3. The 18-34 age group.

  4. With the age increases, the likelihood to buy when made in America also increases.

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. (0.0262+0.0678) x 100%=9.40%.

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. It is inferential because it’s referring to the whole population.

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. 24% (approximately).

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 evidence for the estimation.

Section 2.2

9

  3. 15 times.

  4. 11-7=4, 4 times more.

  5. 15/100=0.15, so 15% of the time.

  6. Almost a bell-shaped.

10

  1. 4 cars.

  2. 9 weeks.

  3. Total frequency is 52 and there’re 9 times two cars were sold, so (9/52) x 100%=17.3%.

  4. It’s skewed to the right, but just slightly.

11

  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. Class 100-109.

  5. Class 150-159.

  6. 8+2+1/200=0.055, so 5.5%.

  7. No.

12

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

  3. class 0-199.

  4. Skewed right.

  5. The statement is wrong because the population base is different, so to be fair, the number facilities should be determined as base on per 300, 500 or 1000 residents. Make sure the number of samples are the same.

13

  1. Skewed right because most of the incomes will be on the left, only a few on the right.

  2. Bell-shaped because most scores will appear around the middle.

  3. Skewed right because most households will around 1-4 people, only a few may exceed thoes numbers.

  4. Skewed left because most patients are older and only a few are younger.

14

  1. Skewed right because not much individuals would consume more drinks per week, most of them consume fewer.

  2. Uniform because the number may be equal in each category.

  3. Skewed left because most of the patients are old and only a few are young.

  4. Bell-shaped because only a few might have a much higher heights or much lower heights.