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.