Section 2.1

7

  1. OF

  2. 15

  3. 15

  4. It might be misleading beacuse each outfield position should be individually reported rather than reported as one.

9

  1. 69%

  2. 55.2 million

  3. Inferential

11

  1. 0.42 ; 0.61

  2. 55+

  3. 18-34

  4. Age increase, likelihood to buy American brand

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. Descriptive statement because it is reporting a result 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 weeks

  3. 0.1730

  4. Skewed Right

11

  1. 200

  2. 10

  3. 60-69, 2; 70-70, 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. 6

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

  3. 0-199

  4. skewed right

  5. The sample size is too small and you can’t compare those two states like that. The statement makes a conclusion based on superficial data. A more fair comparison can be made by comparing each state individually to the US

13

  1. Skewed Right; Most household incomes will be towards the left side, with fewer to the right side

  2. Bell-shaped. Most scores will occur at mid-range, with scores tapering equally in both directions

  3. Skewed Right because most of the time, households have 1-4 occupants. Rarely there is more than 4

  4. Skewed Left because most Alzgheimer patients fall in the older aged category rather than the younger category

14

  1. histogram because you are testing the frequency of a certain activity

  2. skewed right because kids who got to public schools or school in general will be younger

  3. Skewed left, most patients with hearing problems are mostly older

  4. bell shaped because there is a lot a variety in the data for height

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%