1.a. the difference between predictive analytics and prescriptive analytics is the outcome of the analysis. Predictive analytics provides you with the raw material for making informed decisions, while prescriptive analytics provides you with data-backed decision options that you can weigh against one another.

b. nominal data (also known as nominal scale) is a type of data that is used to label variables without providing any quantitative value. … Unlike ordinal data. One of the most notable features of ordinal data is that, nominal data cannot be ordered and cannot be measured.

c. Dataframe and list are two different data structures present in R. Dataframe is widely accepted data structure which is also present in python because of its tabular structure. The dimensions of a dataframe is represented by number of rows and columns while a list is multidimensional.

Dataframes are quite common and used as datasets in modelling and analysis purposes. But lists are very useful in heaping i.e. creating an object that has more than one different objects. Lists are heterogeneous in real sense because not only they can gave different data types like dataframes but also can have different data structures.

2.

- na.omit() & na.pass() – Two functions that help with this task are is.na() which way turns a true value for every NA value it finds and na. omit() that removes any rows that contain an NA value. na.pass returns the object unchanged.
- tail() – show bottom 6 records of data frame by default
- rbind() – R rbind Function. rbind() function combines vector, matrix or data frame by rows. The column of the two datasets must be same, otherwise the combination will be meaningless.
- lm() – the lm(), or “linear model,” function can be used to create a simple regression model. The lm() function accepts a number of arguments (“Fitting Linear Models,” n.d.).
- rep() – the function replicates its values a specified number of times.

3. a.RESULT = data.frame(

name = c(‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’),

score = c(12.5, 9, 16.5, 12, 9, 20, 14.5, 13.5, 8, 19),

attempts = c(1, 3, 2, 3, 2, 3, 1, 1, 2, 1),

qualify = c(‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’)

b. summary(RESULT)

c. dim(RESULT)

d. str(RESULT)

e. RESULT<-RESULT[order(score),]

f. head(RESULT,3)

g. subset(RESULT, attempts==1 & qualify==”yes”)

h. mean(RESULT), max(RESULT), min(RESULT)

4.

5.