Section 2.1

7

  1. The outfield (OF) had the most MVPs

  2. 15 MVPs played 1B.

  3. 15 more OF were MVPs than 1B.

  4. Overall this graph groups all three outfield positions into a single group, which is why it might seem like a lot more outfield players were MVPs than any other position, when in reality, the distribution is more even when the individual outfield positions are taken into account.

9

  1. About 70% of the respondents believe divorce is morally acceptable.

  2. 48 million adults believe divorce is morally wrong.

  3. This is an inferential statement because Gallup is using the statistics gathered from a sample of the American population, to extrapolate the data and apply it to describe the entire population.

11

  1. About 45% of the 18-34 year old population was more likely to buy when made in America, while 60% of the 35-44 yeard old population was more likely to buy when made in America.

  2. The 55+ year old age group has the greatest proportion who are more likely to buy when made in America, with almost 75%.

  3. The 18-34 year old age group has a majority of respondents who are less likely to buy when made in America.

  4. With increasing age, people become more likely 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. 53% of respondents answered always.

  2. About 9% of respondents 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. This is a descriptive statement because the representative is describing the exact sample surveyed instead of extrapolating the data to the entire college population in the United States.

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. About 24% of those surveyed 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"))

  1. The statement attempts to claim that the results of the survey could be applied to the entire nation, when in reality, it could be that only in that region surveyed the Internet habits were as indicated by the survey, but these results could change region to region, state to state, etc.

Section 2.2

9

  1. The most frequent outcome of the experiment was a sum of 8 of the two dice.

  2. The least frequent outcome of the experiment was a sum of 2 of the two dice.

  3. We observed a 7 15 times.

  4. About 7 more 5’s were observed than 4’s.

  5. 15% of the time a 7 was observed.

  6. The shape of this distribution is a normal distribution.

10

  1. The most frequent number of cars sold in a week was 4 cars.

  2. For 9 weeks, two cars were sold.

  3. Total number of weeks: 52 Total number of weeks in which 2 cars were sold: 9 Percentage of time 2 cars were sold: 17.3%

  4. This is a right-skewed distribution.

11

  1. 200 students were sampled.

  2. The class width: Largest data value: 58 Smallest data value: 1 Number of classes: 10 Class width = (58 - 1)/10 = 5.7.

  3. IQ Between 60 and 70: 2 70 and 80: 3 80 and 90: 13 90 and 100: 42 100 and 110: 58 110 and 120: 40 120 and 130: 31 130 and 140: 8 140 and 150: 2 150 and 160: 1

  4. Students who scored between 100 and 110 had the highest frequency.

  5. The student who scored between 150 and 160 had the lowest frequency.

  6. 11 students had an IQ of at least 130, which means that 11/200 or 5.5% of students had an IQ of at least 130.

  7. No.

12

  1. Class Width: Highest data value: 1400 Smallest Data value: 0 Number of classes: 6 Class width = (1400 - 0)/6 = 233.3

  2. Classes: Between 0 and 200 deaths at least 200 deaths at least 400 deaths at least 600 deaths at least 800 deaths at least 1000 deaths at least 1200 but no greater than 1400 deaths.

  3. Between 0 and 200 deaths had the highest frequency

  4. The shape of the distribution is right skewed.

  5. The statement assumes there is a direct causation between the number of alcohol-related deaths and road conditions, when in reality, it could be just because the population in Texas is much larger than the population in Vermont. Thus, a fair way to compare these two states would be to measure percentages of alcohol-related traffic deaths of each state.

13

  1. These are skewed right because there is a minimum household income in this country due to minimum wage, so it is possible to earn any amount of money but there is a lower limit as to how much minimum household income would be.

  2. I would expect this to be bell shaped, since the exam is the same to all students and there will be a majority of students that fall in the middle range of scores, while there will be a few to the left and right of that average.

  3. I would expect this variable to be pretty uniform since in the United States, a standard household of 4 seems to be pretty common.

  4. This variable would be left-skewed because there is a certain age past where it is biologically impossible for that person to be alive, and while Alzheimer’s in younger adults is rare, it is certainly a possibility.

14

  1. This variable is right skewed, since it is impossible to drink less than 0 drinks per week, but any number after that, even though the high volume drinks are less possible, can occur.

  2. I would expect this to be left skewed because at a certain age, people will no longer be young enough to be qualified for school.

  3. This variable is right skewed, since I would expect older age patients to have trouble hearing.

  4. This would be a bell-shaped curve since most heights fall into a majority “average” while a few outliers fall either below or above the average.