Q1. A scatter plot is most suitable for discovering relationships between:
(a) two categorical variables
(b) two continuous variables when data is very dense
(c) two continuous variables when there is no clear functional
   relationship between the two variables
(d) two continuous variables when there is a clear functional
           relationship between the two variables

Q2. A supervised learning algorithm can be used for modelling when  

(a) Nothing is known about the data
(b) Some historical data is available with class labels
(c) It is necessary to predict the future trends from the data
(d) none of the bove

Q3. Support Vector Machine is 

(a) an unsupervised learning algorithm
(b) a logical regression algorithm
(c) a supervised learning algorithm
(d) a data visualization algorithm

Q4. Association rule mining is most applicable in the following  business 
    scenario 

(a) detecting fraudulent transactions using credit cards
(b) classifying product categories
(c) working out the insurance premium payments of potential customers
(d) suggesting additional items when a customer purchases an item

Consider the following R commands. The following three questions are based on these commands. 

> num <- c(12,16,18,132,645,46,25,45)
> name <- c("John", "Emma", "Peter", "Dave", "Jane", "Rob", "Chris", "Emily")
> gender <- c("M","F","M","M","F","M","M","F")
> course <- c("BSC","BSC","BCOM","LAW","MED","MED","BSC","BCOM")
> df= data.frame(num=c(num), name=c(name),gender=c(gender), course=c(course))



Q5. What is the result of the following R command? 

>subset(df, gender =="F" & course !="MED")

(a) Emma     16      F    BSC
    Jane    645      F    MED

(b) num  name gender course
  2  16  Emma      F    BSC
  8  45 Emily      F   BCOM

(c) num  name gender course
    16  Emma      F    BSC
    45 Emily      F   BCOM

(d) none of the above

Q6. What is the result of the following R command? 
>subset(df,gender=="M" | num>100)

(a)  num  name gender course
1  12  John      M    BSC
4 132  Dave      M    LAW
6  46   Rob      M    MED
7  25 Chris      M    BSC

(b)  num  name gender course
1  12  John      M    BSC
2  16  Emma      F    BSC
3  18 Peter      M   BCOM
4 132  Dave      M    LAW
5 645  Jane      F    MED
6  46   Rob      M    MED
7  25 Chris      M    BSC
8  45 Emily      F   BCOM

(c) num  name gender course
1  12  John      M    BSC
3  18 Peter      M   BCOM
4 132  Dave      M    LAW
5 645  Jane      F    MED
6  46   Rob      M    MED
7  25 Chris      M    BSC

(d) none of the above

Q7. What is the result of the following R command? 

> subset(df, num>100, select=c(gender,course))

(a) 
1  12  John      M    BSC
2  16  Emma      F    BSC
3  18 Peter      M   BCOM
4 132  Dave      M    LAW

(b) 
4      M    LAW
5      F    MED

(c) 
4 132  Dave      M    LAW
5 645  Jane      F    MED

(d) none of the above.