Section 2.1

2 number; proportion

3 They should add up to 1.

5

  1. The most common approach is washing their hands. 61% of the population choose this method.

  2. The least used approach is drinking orange juice. Only 2% of the population choose this method.

  3. 25% of the population thinks flu shots are the best way to beat the flu.

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. 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. 243/1025=.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"))

Section 2.2

7 False

8 False

9

  1. 8 was the most frequent outcome.

  2. 2 was the least frequent outcome.

  3. 15 times

  4. About four more 5’s were observed than 4’s.

  5. 15% of the time

  6. bell curve

10

  1. 4

  2. 9 weeks

  3. 17.3% of the time

  4. Skewed right

13

  1. Skewed right because more people are likely to have lower household incomes than higher household incomes.

  2. Bell shaped because most will score in the middle range while few will score high and few will score low.

  3. Skewed right because fewer households will have a higher number of occupants.

  4. Skewed left because people with Alzheimer’s tend to be much older.

14

  1. Skewed right because most individuals will consume a low number of alcoholic drinks per week and less will consume a larger number of alcoholic drinks

  2. Uniform because there will roughly be the same number of students in each age group

  3. Skewed left because those with hearing aids are more likely to be older.

  4. Bell shaped because most full grown men will be around the same height but there will be a few that are shorter than average and taller than average.

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%

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. 18%

  2. 0%

  3. 14%

25

  1. The data is discrete because they are integer numbers. It’s impossible to have a decimal or fraction of a television.

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%

  2. 7.5%