Section 2.1

7

  1. type answer here. OF had the most MVPs as indicsted by the visual highest bar.

  2. type answer here. 15 MVPS as indicsted by where thr bsr reaches for first base.

  3. type answer here. 15 more because the OF bar is 15 units higher than the first base bar.

  4. type answer here. It would be best of the three outfield positions were shown seperatey rather than all in one bar.

9

  1. type answer here. 69% because in total there are 100 people and 69 were from the moreally acceptable so 69/100 is 0.69x100= 69%

  2. type answer here. if there are 240 milion peopel in america then 55.2 nillion believe it is wrong. This is becasue 23 people out of 100 belive it is wrong and so the proportion of that percent for 240 million people makes it come to 55.2 million people believing it is wrong (0.23x240,000,000=55,200,000.

  3. type answer here. It is inferential because it is based on the observations made from the given data on a sample of people.

11

  1. type answer here. 18 to 34: more likely to buy when made in america is 0.42 becasue that is the value shown by the blue bar indicating more likey. 34-44: more likey to buy when made in america 0.61 because that is the value shown by the blue bar indicating more likey.

  2. type answer here. 55+ because it has the biggest gap from more likey and less likely, as well as neither, when comparing the proporiton visually.

  3. type answer here. 18-34 because they have the highest green bar indicated as less likey.

  4. type answer here. As age increasee, one is more likey to buy when made in america.

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. type answer here. 52.72%.

  2. type answer here. 0.262+0.0678= 0.094 so 9.4% 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. type answer here. Inferential because it makes a statement on a more general population scale based on the survey of a particular 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. type answer here. 0.2371 so about 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. type answer here. The statement provides only an estimate but with no confidence.

Section 2.2

9

  1. type answer here. 8

  2. type answer here. 2

  3. type answer here. 15 times

  4. type answer here. 5 was observes 11 times and 4 was observes 7 so 11-7 is 4 so 5 was observed 4 more times than the 4 value.

  5. type answer here. 15/total of 100 observed = 15%.

  6. type answer here. bell shaped curve.

10

  1. type answer here. 12 care sold in a week.

  2. type answer here. two cars were sold for 3 weeks.

  3. type answer here. 3 weeks/10 weeks = 0.3 so 30% of the time 2 cars wwere sold.

  4. type answer here. skewed to the right.

11

  1. type answer here. total of 200.

  2. type answer here. class width is 10 because each number on the x-axis jumps in a unit of 10.

  3. type answer here. Answers are respective. IQ score (class)- 60-69, 70-79, 80-89, 90-99, 100-109, 110-119, 120-129, 130-139, 140-149, 150-159. Frequency- 2,3,13,42,58,40,31,8,2,1.

  4. type answer here. class 100-109 has the highest frequency.

  5. type answer here. class 150-159 has the lowest frequncy.

  6. type answer here. add up 130-160 frequencies and divide by total number of frequency that is 200. So 11/200= 5.5%.

  7. type answer here. No students had an IQ of 165. There were no IQs above 159.

12

  1. type answer here. class width is 200.

  2. type answer here. classes are 0-199, 200-399, 400-599,600-799, 800-999, 1000-1199, 1200-1399.

  3. type answer here. class 0-199 wih highest frequency.

  4. type answer here. skewed right.

  5. type answer here. This statement is wrong becuase it may not be that the reason of more alcohol fatilities are due to safer roads. This is an assumption of causation where merely the two may only have an association.

13

  1. type answer here. skwed right. most households will be in the lower range.

  2. type answer here. bell shape becuase there wll be a high frequency number at particular score and then the super low or high scores.

  3. type answer here. skwed right. households will not contain infinate number of people and they cannot contain 0 if they are considered a household.

  4. type answer here. skwed left because alzheimers likelihood increases with age.

14

  1. type answer here. skwed right. the numbers will probobly not be increasing infinatley and depending on there being differnet drinkers, a bell shape curve is not likey.

  2. type answer here. uniform because there is probobly an even number of each grade.

  3. type answer here. skwed left becuase as you get older is when the need of a hearing aid will come in play.

  4. type answer here. bell shape because there is a relative common height and above and below average 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. type answer here. 50 total households so 12 as number of household wihth children under 5 /50 =24% househilds

  2. type answer here. add together 18 and 12 from 1 household and 2 household with children under 5 (respectivley) and divide by total household 50 = 60% households.