Section 2.1

9

  1. 69%

  2. 55.2 million

  3. Inferential because we use the results of a survey representative of a random sample to make a generalization about the adults in America.

11

  1. 0.42 ; 0.61

  2. 55+

  3. 18-34

  4. As age increases, the tendency to buy products 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. 52.7%

  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 reports the results 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

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"))

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%

d)Right skewed

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. 200

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

  3. 0-199

  4. Right skewed

  5. This statement is erroneous because we need to take into account the populations of Texas and Vermont. We should divide the number of fatalities by the size of the populations separately for Texas and Vermont, and then compare the relative frequencies to arrive to a conclusion.

13

  1. Likely right skewed. Most incomes will be on the left ($50.000 - $150.000) range, with fewer incomes on the right (millions).

  2. Likely bell-shaped. Most scores will be on the middle range and the scores will taper off equally in both directions.

  3. Likely right skewed. Most households will have 1 to 4 occupants, while fewer households will have more occupants.

  4. Likely left skewed. Most Alzheimer patients will be older-aged and fewer patients will be younger.

14

  1. Likely right skewed. Most people will consume around 0-6 drinks in a week, with fewer people consuming more.

  2. Likely uniform. Most kids would be around ages 7-18, and their frequencies would be evenly distributed.

  3. Likely left skewed. Most of the hearing-aid patients will be senior citizens, while fewer patients will be younger.

  4. Likely bell shaped. Most males will be around 5’ 9.5" ft, while fewer males will be very shorter or taller.

15

dattt <- c(16, 18, 12, 3, 1)

rel.freqqq <- dattt/sum(dattt)

categoriesss <- c("Zero", "One", "Two", "Three", "Four")

answerrr <- data.frame(categoriesss,rel.freqqq)

answerrr
##   categoriesss rel.freqqq
## 1         Zero       0.32
## 2          One       0.36
## 3          Two       0.24
## 4        Three       0.06
## 5         Four       0.02

  1. 24%

  2. 60%