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**Subject Code: **BA02

**Subject Name:**

ADVANCED ANALYTICS

USING R

**Component name:**

ASSIGNMENT2

**Question 1:- **Which of these is not a dimension of data quality?

a) Timeliness

b) Completeness

c) Continuity

d) Consistency

**Question 2:- **While implementing k-means algorithm for clustering analysis, which of the following is the correct way of initializing the clustering process?

a) Randomly choose k units from the dataset as the initial cluster means

b) Calculate the distances between the clusters.

c) Generate the dendrogram for the clusters.

d) Find the total number of objects.

**Question 3:- **Which of the following is a mandatory step to estimate n number of β coefficients for a regression model?

a) Take the partial derivative with respect to each β coefficient

b) Equate all independent variables to 0

c) Equate each β coefficient to 0

d) Take the partial derivative with respect to each independent variable

**Question 4:- **Suppose a logistic regression model is formulated with three variables: D, X1, X2. The D variable is dependent on X1 and X2. The X1 and X2 variables also have some kind of interaction that affects the value of a.

Which of the following term will you add in the equation to include the interaction between X1 and X2? a) X1+x2

b) X1*X2

c) X1/X2

d) X1*X1+X2*X2

**Question 5:- **Which of these is an activity a data warehouse should be able to do? a) Organize data

b) Manipulate data

c) Integrate data

d) All of the above

**Question 6:- **In model selection and fitting for forecasting, what is fitting?

a) Estimating the known model parameters, usually by the method of least squares.

b) Estimating the unknown model parameters, usually by the method of least squares.

Estimating the unknown model parameters, usually by the method of highest squares

None of the above

**Question 7:- **Which of these forecasting techniques are subjective in nature?

Quantitative

Qualitative

Both a and b

- None of the above

**Question 8:- **In which of these areas is forecasting primarily important?

Operations

Marketing

Demography

All of the above

**Question 9:- **The a priori property states that if an itemset Z is not frequent, then adding another item A to the itemset Z will not make Z more frequent

FALSE

TRUE

May be either a or b

Not coming in preview of apriority property rules

**Question 10:- **Which of these can the generalized rule induction (GRI) handle as inputs?

Categorical variable

Numerical variable

Both a and b

None of the above

**Question 11:- **A model may be descriptive or inferential.

Yes

No

All models are statistical

None of the above

**Question 12:- **Which of these best define a model?

A global description or explanation of a data set, taking a high level perspective.

Local features of the data

Insight drawn from a series of data

Visualization of data related to various categories

**Question 13:- **Association rule mining can be applied either in a supervised or an unsupervised manner. 1 True , 2

TRUE

False

Both a and b

Unsupervised variable is applied on No target variable

**Question 14:- **The two main categories of DB engines used in the Operational Databases layer of the Big Data stack are:

a) Columnar and key-value pair

b) RDBMS and NoSQL

c) Columnar and document databases

d) Risk and MongoDB

**Question 15:- **ETL tools are a part of which layer of the Big Data technology stack? a) Application Layer

b) Network Layer

c) Security Layer

d) Organizing data services and tools layer

**Question 16:- **Which of the following is a Big Data application used for log data analysis? a) Dataxu

b) Bluefin

c) Splunk

d) Myrrix

**Question 17:- **Which of these services/tools is needed for the loading and conversion of Data? a) Distributed Files System

b) Extract, transform, and load (ETL)

c) Workflow services

d) All of the above

**Question 18:- **Which of these method is a way to identify outliers for numeric variables? a) Histograms

b) Scatter plots

c) Both a and b

d) None of the above

**Question 19:- **Removal of which of these is a key reason to pre-process data? a) Missing values

b) Outliers

c) Redundant fields

d) All of the above

**Question 20:- **Which of these is NOT a key concept in time series analysis? a) Trend

b) Volume

c) Serial dependence

d) Stationary

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