Section 2.1

  1. Number, frequency

  2. Relative frequency distrubution should add up to 1

5

  1. Most common approach is washing hands. This is chosen by 61% of the population.

  2. The least common approach is is drinking orange juice, which is only used by 2% of the population.

  3. 25% of people think flu shots are the best thing to beat the flu.

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. 52.72% answered “always”

  2. 9.40% answered “Never or rarely”

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. It is an inferential statement.

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. 23.7 or 4% never use the internet.

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

  1. False

  2. True

9

  1. 8 had the most frequent outcome

  2. 2 had the least frequent outcome

  3. Value 7 was observed 15 times

  4. The value of 5 was observed 4 more times than the value 4

  5. Value 7 was observes 15% of the time

  6. The distribution is bell shaped

10

  1. 4 is the most frequent number of cars sold in a week.

  2. 2 cars were sold for 9 weeks

  3. 2 cars were sold for 17% of the time

  4. The distribution is skewed right

13

  1. Right skewed becuase most incomes will be more toward the left side, with fewer but greater incomes to the right

  2. Bell shaped becuase the same amuont of students who do poorly is about equal to the amount that do well on the SAT with majority of scores in the middle

  3. Right skewed because most households will have less than 5 members in the home, but families with larger members exist, but there are less of them

  4. Left skewed because most Alzheimer’s patients are older in age, as opposed to fewer young patients.

14

  1. Right skewed becuase more people have drinks fewer days in a week than people who have drinks almost every day of the week

  2. Right skewed because there are more young students than old.

  3. Left skewed because patients hard of hearing tend to be on the older side.

  4. Bell shaped becuase most men are middle height, with the equal

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% of households have 2 children under the age of 5

  2. 60% of households have 2 or 1 children under the age of 5

16

free_throws <- c(16, 11, 9, 7, 2,3,0,1,0,1)

rel.freqqq <- free_throws/sum(free_throws)

categoriesss <- c("1", "2", "3", "4","5","6","7","8","9","10")

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

answerrr
##    categoriesss rel.freqqq
## 1             1       0.32
## 2             2       0.22
## 3             3       0.18
## 4             4       0.14
## 5             5       0.04
## 6             6       0.06
## 7             7       0.00
## 8             8       0.02
## 9             9       0.00
## 10           10       0.02

  1. 14%

  2. 2%

  3. 90%

25

  1. Discrete, because it is possible to count the number of televisions

tv <- c(1, 1, 1, 2, 1,
        1, 2, 2, 3, 2,
        4, 2, 2, 2, 2,
        2, 4, 1, 2, 2,
        3, 1, 3, 1, 2,
        3, 1, 1, 2, 1,
        5, 0 ,1, 3, 3,
        1, 3, 3, 2, 1)

#table(tv)

tv <- c(1,14,14,8,2,1)

tv.freq <- tv/sum(tv)

tv.cat <- c("0", "1", "2", "3","4","5")

freq.tab <- data.frame(tv.cat,tv)
rfreq.tab <- data.frame(tv.cat,tv.freq)


freq.tab
##   tv.cat tv
## 1      0  1
## 2      1 14
## 3      2 14
## 4      3  8
## 5      4  2
## 6      5  1
rfreq.tab
##   tv.cat tv.freq
## 1      0   0.025
## 2      1   0.350
## 3      2   0.350
## 4      3   0.200
## 5      4   0.050
## 6      5   0.025

  1. 20% had 3 TV’s

  2. 7.5% had 4 or more TV’s