Section 2.1

7

  1. OF

  2. 15

  3. 15

  4. The OF position is three times more likely to have an MVP because there are probably three times as many people in OF positions.

9

  1. 69%

  2. 55.2 Million

  3. The statistic is descriptive, because it is interpretting the data.

11

  1. 0.52; 0.61

  2. 55+

  3. 18-34

  4. As age increases, increased likelihood to buy when made in America 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%

  2. 9%

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.

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. 23%

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 sample size may not be large enough to generalize the data to the population

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. 4

  5. 15%

  6. normal

10

  1. 4

  2. 9

  3. 17%

  4. Normal

11

  1. 200

  2. 100

  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. 94.5%

  7. No.

12

  1. 1400

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

  3. 0-199

  4. skewed right

  5. The assumption is wrong because there may be an underlying factor of population.

13

  1. Skewed right. There are very few people who make a high income.

  2. Normal. There should be a high concentration of scores in the middle and tapering concentrations on both ends.

  3. Skewed right. There are fewer households with a large number of residents than with about 3-4.

  4. Skewed left. Fewer young people are diagnosed with Alzheimer’s

14

  1. Normal. There are a variety of people who drink various amounts.

  2. Uniform. There are about the same frequencies of students per age

  3. Skewed left. There are fewer young people with hearing aids.

  4. Normal. There are equal outliers on both sides of the curve.