Section 2.1

2 number, proportion

3 one

5

  1. washing hands-61%

  2. drinking orange juice-2%

  3. 25%

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. never-2.6%, rarely-6.9%, together-9.5%

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 descriptive b.c it shows the resuslts of a 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. 23.75 OR 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

7 FALSE

8 FALSE

9 type answer here

  1. 8

  2. 2

  3. 15

  5. 15%.

  6. BELL-SHAPED.

10

  3. 17.3%.

  4. SKEWED RIGHT

13

  1. SKEWED RIGHT B.C AVERAGE HOUSEHOLDS WILL BE BETWEEN 50,000-150,OO0.

  2. ITS BELL SHAPED B.C THEY WILL BE AROUND THE AVERAGE.

  3. IT IS SKEWED RIGHT BECAUSE THE HOUSEHOLDS DONT HAVE MUCH MORE THAN 2-4 PEOPLE TYPICALLY .

  4. IT IS SWEED LEFT BC MOST POEPLE DIAGNOSED ARE OLDER.

14

  1. SKEWED RIGHT BECAUSE MOST PEOPLE DONT DRINK EVERY NIGHT.

  2. UNIFORM BC THERE I ABOUT THE SAME AMOUNT OF STUDENTS IN EACH GRADE.

  3. SKEWED LEFT BC MOST PEOPLE WITH HEARING AIDS ARE OLDER.

  4. IT IS A BELL SHAPE BC MOST MEN ARE 5’8TO 5’10 IN 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%.

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. 14%.

25

  1. IT is discrete bc yo ucant have half of a tv.

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%