Section 2.1

9

  1. 69%

  2. 55,200,800

  3. Inferential, since a generalisation has been made from the given data in the graph and it would be correct to say so from the inference.

11

  1. 18-34 year olds: .44 ; 35-44 year olds: .61.

  2. 55+ age group

  3. 18-34 year old age group

  4. As shown in the graph the older the people start getting their likelyhood to buy the products made in America keeps increasing

13

  1. Never: 0.0262 = 125/4776 Rarely: 0.0678 = 324/4776 Sometimes: 0.1156 = 552/4776 Most of the time: 0.2632 = 1257/4776 Always: 0.5272 = 2518/4776
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. 52.721%

  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. Inferential Statement, since an inference from the data provided is given.

15

  1. more than 1 hour :0.3678 = 377/1025 up to 1 hour a day:0.1873 = 192/1025 a few times a week:0.1288 = 132/1025 a few times a month or less:0.0790 = 81/1025 never :0.2371 = 243/1025
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 / 23.7%

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. Approximately bell shaped or a-symetrical

10

  1. 4

  2. 9

  3. 17.3%

  4. skewed right

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 has the highest frequency of 58

  5. 150-159 has the lowest frequency of 1

  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. The statement by the reporter is incorrect because the topic of discussion isnt about roads being safer or not its about alcohol-related traffic fatalities. If the reporter stated that people dont drink and drive very often in vermont or the authorities play a better role in making sure that alcohol-related fatalities are kept in control it wouldve been a better statement. A fair comparison can be made through the population of both the towns, keeping that in mind than the number of people who are above the legal drinking and how many can drive the car. keeping all these variables in mind a fair comparison can be made.

13

  1. Right Skewed: annual household income will be right skewed because there will be a few thousands of people below the average, the maximum at the average and alot more people above the average making the graph right skewed.

  2. bell shaped: because there will be people below the average and above the average with the maximum at the average and thus, making it a bell shaped histogram for standardized tests such as sat

  3. skewed right: most household will have 1-4 occupants with really few exceptions of having more than that and thus, it should be skewed right.

  4. left skewed: since the onset of alzheimers beigns after a certain age, there are always complication such as somebody may be diagnosed at 40 and somebody at 70 but there will be alot of people below the average

14

  1. right skewed: because there will be people starting at 0 or 1 and then the number of people having more and more will keep going down thus, itll be right skewed

  2. uniform: this is most likely to be uniform because in a public school district every class size should be almost same without alot of differentiation between them because schools will have a maximum capacity of students in each class. thus, it should be uniform

  3. left skewed: this is so because there will be very few people in younger ages having problem with hearing and thus it will keep scattering at points on the left side of the average age

  4. bell- shaped: with the average being the maximum there will be exceptions on both sides of the graph and thus, it should be a bell shaped or a-symetrical graph

15

  1. 0: 0.32 =16/50; 1: 0.36 =18/50; 2: 0.24 =12/50; 3: 0.06 =3/50; 4: 0.08 =1/50;
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%