7a China had the largest amount of internet users. 7b The United Kingdom had approximately 50 million Internet users in 2010. 7c There were approximately 350 million more users in China than in Germany in 2010. 7d This graph may be misleading because it does not use relative frequencies, it is not a histogram. Therefore the results may appear more drastic than they truly are.

9a 69% of the respondents to the survey found divorce to be morally acceptable. 9b If there were 240 million adult Americans in 210, 55.2 million of them would theoretically believe that divorce is morally wrong. 9c Gallup’s statement would most definitely be inferential because he is applying data from a small experimental sample to a much larger population.

11a 42% of 18-34 year old respondents are more likely to buy products when they are advertised as having been made in America. However, 61% of of adults aged 35-44 are more likely to buy American made products. 11b Individuals aged 55 and over are most likely to buy products being advertised as Made in America. 11c The cohort of 18 to 34 years olds has a majority of respondents who are less likely to buy products that have been Made in America. 11d It appears that as American citizens grow older, they become more passionate about and likely to buy products that have been made in America.

13

my_data <- c(.0262,.0678,.1156,.2632,.5272)

groups <- c("never", "rarely", "sometimes", "mostly", "always")

barplot(my_data, main = "College Students Wearing Seatbelts as Passengers",names.org = groups, col =c("red","blue","green","yellow","purple"))
## Warning in plot.window(xlim, ylim, log = log, ...): "names.org" is not a
## graphical parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "names.org" is not a graphical parameter
## Warning in axis(if (horiz) 1 else 2, cex.axis = cex.axis, ...): "names.org"
## is not a graphical parameter

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "College Students Wearing Seatbelts As Passengers", names.arg = groups, col = c("red", "blue", "green", "yellow", "purple"))

13b 2518/4776=.527=52.7% of respondents answered “always” 13c 449/2518=.94=9.4% 13f

my_data <- c(9.422,24.422,41.61,94.75,189.798995)

# Simple Pie Chart
slices <- c(9.422,24.422,41.61,94.75,189.798995)
lbls <- c("Never", "Rarely", "Sometimes", "Most of the time", "Always")
pie(slices, labels = lbls, main="How Often Young People Wear Seatbelts")

13g This is once again an inferential statement because results from an experimental group is being applied to an entire population. Something is being inferred.

15a

my_data <- c(.367,.187,.128,.2632,.5272)

groups <- c("More than 1 hour a day", "Up to 1 hour a day", "A few times a week", "A few times a month or less", "never")

barplot(my_data, main = "Frequency of Internet Usage",names.org = groups, col =c("red","blue","green","yellow","purple"))
## Warning in plot.window(xlim, ylim, log = log, ...): "names.org" is not a
## graphical parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "names.org" is not a graphical parameter
## Warning in axis(if (horiz) 1 else 2, cex.axis = cex.axis, ...): "names.org"
## is not a graphical parameter

rel_freq <- my_data / sum(my_data)

barplot(rel_freq, main = "Frequency of Internet Usage", names.arg = groups, col = c("red", "blue", "green", "yellow", "purple"))

15b This statement is obviously incorrect because a total of 1025 individuals were interviewed, and 377 said they used the internet more than one houra day. 377/1025= 37%, however, the news reporter did not account for all the adults that said they used it a few times a week and few times a month, which are all values of time much greater than one hour.

Switching to 2.2 questions 9a The value of 8 was the most frequent outcome of the experiment. 9b The least frequesnt value to come out of this experiment was 2. 9c We observed 7 as a result a total of 15 time. 9d We observed approximately 4 more fives than fours. 9e 7 was observed as a result about 15% of the time 9f This distribution takes on a bell shape.

10a The most frequent number of cars sold in a week is four. 10b Two cars were sold each week for a total of 9 weeks. 10c Two cars were sold each week approximately 17.3% of the time. 10d The distribution is skewed right.

11a A total of 200 students were sampled. 11b The width of the class was 10. 11d The class of 100-109 has the highest frequency at 58. 11e The class of 150-159 has the lowest frequency at 1. 11f Only 5.5% of pstudents had an IQ of at least 130. 11g No, no students had an IQ of at least 65 according to this study.

12a The class width is 200 fatalities. 12b. The classes are 0-199,200-399,400-599, 600-799, 800-999, 1000-1999, 1200-1399, 1400-1599. 12c 0-100 is the class with the highest frequency. 12d The distribution is skewed right. 12e. This statement is very misleading because Vermont is a much more rural area than Texas and is less densely populated, thus it makes sense that they would have fewer alcohol-related car fatalities.

13a Annual household income in the US would most likely be skewed right. 13b Scored on a standardized test would also most likely be bell shaped. 13c The amount of people living in a given household would probably be skewed to the right. 13d The ages of people diagnosed with alcohol would most likely be skweed left.

14a Thee amount of alcoholic drinks would most likely be skewed to the right. 14b The aes of students in a public school different would probably be a uniform distribution. 14c The ages of individuals with hearing aids would most likely be skewed left. 14d The heights of full grown men would probably be bell shaped.