1. China

  1. 50 million users

  2. 360 million more users

  3. The data shown is in frequency, not relative frequency. This data can be misleading because the sample sizes or population sizes can be different for each country, such as China that has such a large population.

    1. 69 %

  1. 55200000 people

  2. Inferential - because it is more generalized - they are applying it to the ENTIRE adult population and not just giving data about a specific group. They are talking about the population, which is why it is not an example of descriptive data.

    1. 0.42 ; 0.61

  1. 55+

  2. 18-34

  3. As age increases, people become more likely to buy things when made in America.

    1. The total number of college students is 4776 The r.f of Never is 0.0262 The r.f of Rarely is 0.0678 The r.f of Sometimes is 0.1156 The r.f of Most of the time is 0.2632 The r.f of Always is 0.5272

  1. 52.7%

  2. 2.62% answered Never and 6.78% answered Rarely. So total is 9.4%

my_data<-c(0.0262,0.0678,0.1156,0.2632,0.5272)
group<-c("Never","Rarely","Sometimes","MostofTime","Always")
barplot(my_data, names.arg=group, main="Answer13e")

my_data<-c(125,324,552,1257,2518)
group<-c("Never", "Rarely", "Sometimes", "MostofTime", "Always")
barplot(my_data, names.arg=group, main="Answer13d")

my_data<-c(9.432,24.408,41.616,94.752,189.792)
group<-c("Never", "Rarely", "Sometimes", "MostofTime", "Always")
pie(my_data, labels=group, main="Answer13f")

  1. This is an inferential data statement because they take the entire college population into account

15)a) The total is 1025 The r.f for More than 1 hour a day is 0.3678 The r.f for Up to 1 hour a day is 0.1873 The r.f for A few times a week is 0.1288 The r.f for A few times a month or less is 0.0790 The r.f for Never is 0.237

  1. 23.7%

  2. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

my_data<-c(377,192,132,81,243)
group<-c("MoreThan1Hr", "Upto1Hr", "FTAWeek", "FTAMonth", "Never")
barplot(my_data, names.arg=group, main="Answer15c")

my_data<-c(0.3678,0.1873,0.1288,0.0790,0.237)
group<-c("MoreThan1Hr", "Upto1Hr", "FTAWeek", "FTAMonth", "Never")
barplot(my_data, names.arg=group, main="Answer15d")

my_data<-c(132.408,67.428,46.368,28.44,85.32)
group<-c("MoreThan1Hr", "Upto1Hr", "FTAWeek", "FTAMonth", "Never")
pie(my_data, labels=group, main="Answer15e")

  1. No level of confidence is provided with the statement

2.2 9) a) 8

  1. 2

  2. 15 times

  3. 4 more times

  4. 15%

  5. Bell-Shaped

    1. 4

  1. 9

  2. 17%

  3. Skewed Right

    1. 200 students

  1. 10

  2. 60-69 is 2, 70-79 is 3, 80-89 is 13, 90-99 is 42, 100-109 is 58, 110-119 is 40, 120-129 is 31, 130-139 is 8, 140-149 is 2, 150-159 is 1

  3. 100-109

  4. 150-159

  5. 5.5%

  6. No

    1. 200

  1. 0-199, 200-399, 400-599, 1000-1199, 1400-1599

  2. 0-199

  3. Skewed Right

    1. Most likely will be skewed right. This is because more people will have household incomes in the lower and medium range and fewer will have incomes in the higher ranges - such as there are very few millionaires

  1. Most likely bell shaped. This is because such tests try to get kids in the medium range and very few get very low or very high scores.

  2. Most likely skewed right because most households will have fewer number of kids and most households have a greater number of people such as ten

  3. Most likey skewed left because this disease occurs in older patients

    1. Most likely bell shaped because very few people may either drink excessively or not drink at all with the average being in the middle range

  1. This will most likely be bell shaped because public schools usually accept kids of all ages

  2. Most likely skewed left because hearing starts to deteriorate as people get older

  3. Most likely bell shaped - very few are very short or very tall so tapering occurs towards the ends

hist(iris$Sepal.Length)

The shape of Iris Data Graph is most likely bell shaped.

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