Section 2.1

7

  1. OF

  2. 15

  3. 15

  4. It lumps all of those positions into one category, making it much larger than others because the others only count for one position.

9

  1. ~ 68%

  2. 55,200,000

  3. Inferential because it is only that statistic for the sample, and it could probably be more for the population..

11

  1. Around 42% of 18-34 yr olds and Around 61% of 35-44 yr olds.

  2. The 55 + group

  3. 18-34 group

  4. If you’re younger, you’re less likely to buy things made in America, and if you’re older, the opposite is true.

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. a/b 53%

  2. a/b 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, because it mentions that this percentage is in regards to the sample rather than the 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. 0.24

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. They did not clarify that this was from the sample- they made it descriptive when it should be inferential.

Section 2.2

9

  1. 8

  2. 2

  3. 15

  4. ~4 more times

  5. 15%

  6. Slightly left-skewed.

10

  1. 4

  2. 9

  3. 17%

  4. Skewed right

11

  1. 200

  2. 28.5%

  3. 2 classes: 60-70 3 classes: 70-80 13 classes: 80-90 42 classes: 90-100 58 classes: 100-110 40 classes: 110-120 31 classes: 120-130 8 classes: 130-140 2 classes: 140-150 1 class: 150-160

  4. 100-110

  5. 150-160

  6. 5.5%

  7. No

12

  1. 0.56

  2. 32: 0-200 15: 200-400 2: 400-600 1: 600-800 1: 800-1000 0: 1000-1200 1: 1200-1400

  3. 0-200

  4. Right-Skewed

  5. There could be other variables- such as the year or perhaps the population of the state.

13

  1. Skewed right- typically there is less large incomes than larger ones.

  2. Bell-curve- typically the SAT wants for a range of scores, with most in the middle.

  3. Probably a bell-curve- a house is typically lived in by a family, which could have fewer or more people, but probably somewhere between 3-6 people.

  4. Skewed left- definitely more likely to get this disease the older a person gets.

14

  1. Skewed right- probably on the lower end of the number scale if it’s the general population.

  2. Skewed right- students are probably in the range of 5-18, and if it’s everybody, then those would be the ages for elementary or high school.

  3. Skewed-left- the older the age, the more likely people are to have hearing aids.

  4. Bell-curve- typically the mean and the median are very similar in this case as by this point the men have stopped growing so there is less diversity.