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Data Warehousing amp Data Mining . Which of the following is the . A database is a
collection of a.
Related data most popularly available and rich b. Interrelated data information repositories c.
Irrelevant data a. Temporal databases d. Distributed data b. Relational databases . A
Relational database is a c. Transactional databases collection of d. spatial databases .
Which of the following a. tables b. events databases is used to store timec. attributes related
data d. values a. Spatial databases . A is a repository of b. Text databases information
collected from c. Multimedia databases d. Temporal databases multiple squares stored
under a . From a DWH perspective, data unified schema, and which usually mining can be
viewed as an resides at a single site. a. Data mining advanced stage b. Database of c. Data
warehouse a. OnLine Transaction Processing d. legacy databases b. OnLine Data
Processing . Which of the following c. OnLine Analytical Processing databases is used to
store image, d. OnLine Electronic Processing . A is a group of audio, and video data a.
Heterogeneous databases heterogeneous databases b. Temporal databases a. Time series
databases c. Legacy databases b. Object oriented databases d. Multimedia databases c.
Legacy databases . What is the single dimensional d. Spatial databases . Spatial databases
includes association rule for the following a. Legacy databases predicatenotation, which in b.
Time series databases multidimensional association rule. c. Satellite image databases
ContainsT, quotcomputerquot d. Temporal databases containsT, quotsoftwarequot . Many
people treat data mining a. Computer software as synonym for another popularly b. Software
computer used term c. Software computer a. Knowledge Discovery in d. Computer software
databases . Which of the following analysis b. knowledge inventory in databases attempt to
identify attributes that c. Knowledge acceptance in databases d. knowledge disposal in
databases.
do not contribute to the classification or prediction process a. Cluster analysis b. Outlier
analysis c. Relevance analysis d. Evolution analysis . Which of the following is a
summarization of the general characteristics or features of a target class of data a. Data
discrimination b. Data characterization c. Data compression d. Meta data . is a comparison of
the general features of target class data objects with general features of objects from one or
a set of contrasting classes. a. Data characterization b. Data summarization c. Data
discrimination d. Meta data . interestingness measures are based on user beliefs in the data.
a. Objective b. Descriptive c. Collective d. Subjective . mining tasks characterize the general
properties of the data in the databases. a. Descriptive b. Predictive c. Metadata d. Data
. mining tasks perform inference on the current data in order to make predictions. a.
Descriptive b. Predictive c. Data d. Metadata . The derived model may be represented in the
form of a. ER model b. Flow chart c. Decision trees d. DFD . Which of the following is the
classification of data mining systems a. Summarization b. Visualization c. Discrimination d.
Characterization . analysis describes and models regularities or trends for objects whose
behavior changes over time. a. Data evolution b. Cluster c. Outlier d. Summarization . Which
of the following issues relation to the diversity of database type a. Handling noisy or
incomplete data b. Incorporation of background knowledge c. Handling of relational and
complex types of data d. Efficiency and scalability of data mining algorithms . Which of the
following is not
m major issue in data mining . Mining different kinds of knowledge in databases is an issue in
a. Performance issue b. Mining methodology and user interaction issues c. Diversity of
database types issues d. time complexity . Pattern evolution is an issue related to a. Mining
methodology and user interaction issues b. Performance issues c. Issues relating to the
diversity of database types d. Issues relating to the Measurement . A DWH is a subject
oriented, integrated, time variant, and collection of data in support of managements
decisionmaking process. a. Nonvolatile b. Volatile c. Disintegrated d. Object oriented . An
system focuses mainly on the current data with in an enterprise or department, without
referring to historical data or data in different organizations . a. OnLine Analytical Processing
b. OnLine Data Processing c. OnLine Electronic Processing d. OnLine Transaction
Processing . The basic characteristic of Online Analytical Processing is a. Informational
processing b. Operational processing c. Data processing d. Data cleaning
a. Mining methodology and user interaction issues b. Performance issues c. Issues relating
to the diversity of database types d. Issues relating to the Measurement . Processing queries
in operational databases would substantially degrade the performance of operational tasks.
a. OnLine Transaction Processing b. OnLine Electronic Processing c. OnLine Data
Processing d. OnLine Analytical Processing . An System typically adopts either a star or
snow flake model and subject oriented database design. a. OnLine Transaction Processing
b. OnLine Electronic Processing c. OnLine Analytical Processing d. OnLine Data Processing
. The access patterns of an system consist mainly of short, atomic transactions. a. OnLine
Analytical Processing b. OnLine Transaction Processing c. OnLine Electronic Processing d.
OnLine Data Processing . Which of the following approach requires complex information
filtering and integration processes and competes for resources with processing at local
sources a. Updatedriven approach b. Integratedriven approach c. Querydriven approach d.
Datadriven approach
. Which of the following cuboid that holds the highest level of summerization a. Cuboid b.
Base cuboid c. Nonbase cuboid d. Apex coboid . is a visualization operation that rotates the
data axes in view in order to provide an alternative presentation of the data a. Rollup b. Drill
down c. Pivot d. Slice amp dice . tables can be specified by users or experts, or automatically
generated and adjusted based on data distributions. a. Fact b. Summarized c. Dimension d.
Relational . executes queries involving more than one fact table a. Drillthrough b. Drillacross
c. Drilldown d. Rotate . A allows data to be modeled and viewed in multiple dimensions. a.
Meta data b. Data cube c. Database d. Fact table . The major difference between the
snowflake and star schema models is that the dimension tables of the snowflake model
image kept in
form a. Standard b. Denormalized c. Normalized d. Multi dimensional . Which of the following
is not a measure, which is based on the kind of aggregation functions used. a. Cumulative b.
Distributed c. Algebraic d. Holistic . A concept hierarchy that is a total or partial order among
attributes in database schema is called a hierarchy. a. Setgrouping b. Grouping c. Decision
d. Schema . Which of the following focuses on socioeconomic applications a. Statistical
database systems b. Online Analytical Processing systems c. Spatial database systems d.
Temporal database systems . A model consists of radial lines emanating from a central point,
where each line represents a concept hierarchy for a dimension a. Cube net b. Triangle net
c. Square net d. Star net . Which of the following is constructed where the enterprise
warehouse is
the sole custodian of all warehouse data. Which is then distributed to the various dependent
data marts. a. Enterprise DWH b. Two tier DWH c. Multitier DWH d. Virtual warehouse .
Which of the following is a Multi Dimensional Online Analytical Processing a. Ess base b.
Database c. Swiss base d. Red brick . The view includes fact tables and dimension tables. a.
DWH b. Topdown c. Data source d. Business Query . Which of the following is a Hybrid
OLAP server a. MS SQL server . b. MS SQL . c. MS SQL server . d. MS SQL server . . ETL
stands for a. Evaluate, Transport and Link b. Extract Transfer and Load c. Error, Tracking
and Load d. Extract, Transient and Load . To architect the DWH, the major driving factor to
support is a. An inability to cope with requirements evolution b. Not populating the
warehouse c. Day to day management of the warehouse d. Supporting Online Transaction
processing . A contains a subset of corporatewide data that is of value to
a specific group of users. a. Enterprise warehouse b. Virtual warehouse c. Data warehouse
d. Data mart . A is a set of views over operational databases a. Enterprise warehouse b.
Virtual warehouse c. Data warehouse d. Data mart . What kind of the intermediate servers
that stand in between a relational backend server and client frontend tools a. Hybrid OLAP
servers b. Multidimensional OLAP server c. Relational OLAP servers d. Specialized SQL
servers . Choose the that will populate each fact table record a. Measures b. Dimensions c.
Grain d. Business Process . How many cuboids are there in an n dimensional data cube a. b.
c. d. . Meta data repository contains a. Operational meta data b. Data irrelevant to system
performance c. The mapping from the DWH to the operational environment d. Summarized
data . Which of the following support the bitmap indices a. Sybase IQ
b. Oracle c. CoBoL d. SQL . are created for the data names and definitions of the given
warehouse a. Data cube b. Summarized data c. Meta data d. Detailed Information .
Chunking technique involves quotoverlappingquot some of the aggregation computations, it
is referred to as aggregation in data cube computation a. Two way array b. Three way array
c. Multi way array d. Sparse array . The operator computes aggregates over all subsets of
the dimensions specified in the operation. a. Data base b. Computer cube c. Define cube d.
Group by . Which of the following is a subcuge that is small enough to fit into the memory
available for cube computation a. Bulk b. Array c. Structure d. Chunk . The bit mapped join
indices method is an integrated form of a. Composite join indexing and bitmap indexing
b. Join indexing and composite join indexing c. Join indexing and bitmap indexing d. Bitmap
indexing and outer join indexing . A set of attributes in a relation schema that forms a primary
key for another relation schema is called a a. Primary key b. Foreign key c. Secondary key d.
Composite key . Which of the following typically gathers data from multiple, heterogeneous,
and external sources a. Data cleaning b. Load c. Refresh d. Data extraction . OLAM is
particularly important for the following reason a. How quality of data in DWH b. Data
processing c. OLTPbased exploratory data analysis d. Online selection of data mining
functions . Which of the following sets a good example for interactive data analysis and
provides the necessary preparations for exploratory data mining a. OLP b. OLAP c. OLTP d.
OLDP . Which of the following is not
exception indicator
a. Out Exp b. Self Exp c. In Exp d. Path Exp . can help business managers find and reach
more suitable customers, as well as gain critical business insights that may help to drive
market share and raise profits. a. Data warehouse b. Data mining c. Data summarization d.
Data processing . is an alternative approach in which precomputed measures indicating data
exceptions are used to guide the user in the data analysis process at all levels of
aggregation. a. Hypothesisdriven exploration b. Inventorydriven exploration c.
Discoverydriven exploration d. Exceptiondriven exploration . Which of the following is an
exception indicator that indicates that indicates the degree of surprise of the cell value,
relative to other cells at the same level of aggregation a. Out Exp b. In Exp c. Path Exp d.
Self Exp . is a powerful paradigm that integrates OLAP with data mining technology. a.
Online Analytical Modeling b. Online Analytical Machine
c. Online Analytical Mining d. Online Analytical Monitoring . Data warehouse application is a.
Data Processing b. Transaction Processing c. Datacube d. Datamining . cubes compute
complex queries involving multiple dependent aggregates as multiple granularities a. Multi
feature b. Data c. Meta d. Solid . Which of the following performs a linear transformation on
the original data a. Zscore normalization b. Normalization with decimal scaling c.
Zerostandard deviation d. Minmax normalization . Which of the following is the best method
for missing values in data cleaning a. Fill in the missing value manually b. Use the most
probable value to fill in the missing value c. Use the attribute mean to fill the missing value d.
Use a global constant to fill in the missing value . The minimum and maximum values in a
given bin are identified as the a. Bin means b. Bin average c. Bin medians d. Bin boundaries
. Which of the following is data transformation operation a. Normalization b. Regression c.
Clustering d. Binning . The correlation between attributes A and B can be measured by a. b.
c. d. . methods smooth a sorted data value by consulting in neighborhood ie the values
around it. a. Clustering b. Binning c. Regression d. Data reduction . Zscore normalization is
also called as a. Minmax normalization b. Zerostandard deviation normalization c. Zeromean
normalization d. Normalization by decimal scaling . is a random error or variance in a
measured variable. a. Bin b. Cluster c. Noise d. Regression . The data are consolidated into
forms appropriate for mining is called as a. Data reduction b. Data Redundancy c. Data clean
d. Data transformation . Which of the following is a decision tree algorithm a. C. b. ID
c. PP d. DIM . If the tuples in D are grouped into M mutually disjoint Clustering, then an
simple random sample of m clusters can be obtained, where m M which of the following suits
the above sentence a. Stratified sample b. SRS without replacement c. Cluster sample d.
SRS with replacement . Multidimensional index trees include a. A trees b. Ttrees c. Ptrees d.
Rtrees . Which of the following strategy for data reduction is irrelevant, weakly relevant, or
redundant attributes may be detected and removed a. Data cube aggregation b. Dimension
reduction c. Data compression d. Numerosity reduction . In database systems, are primarily
used for providing fast data access. a. Redblack trees b. Game trees c. Multidimensional
index trees d. splay trees . If the mining task is classification, and the mining algorithm itself
is used to determine the attribute subset, then this is called a approach.
a. Filter b. Reduction c. Smoothing d. Wrapper . The discrete wavelet transformation is
closely related to the transform. a. Discrete fourier b. Fourier c. Laplace d. wavelet . Principal
components analysis is also called as a. Karhunenloeve method b. Kinenliva method c.
Kruskallearn method d. Kutnilara method . can be used as a data reduction technique since it
allows a large data set to be represented by a much smaller random subset of the data. a.
Clustering b. Regression c. Histograms d. Sampling . Loylinear models are a. Parametric
methods b. Discrete methods c. Nonparametric methods d. Non discrete methods . Which of
the following method is the generation of concept of hierarchies for categorical data a.
Specification of a portion of a hierarchy by implicit data grouping b. Specification of their
partial ordering, but not of a set of attributes
c. Specification of a set of attributes, but not of their partial order d. Specification of only a
partial set of entities . Which of the following method uses class information a. Histogram
analysis b. Binning c. Cluster analysis d. Entropybased Discretization . hierarchies for
categorical attributes or dimensions typically involve a group of attributes a. Diccretization b.
Semantic c. Index d. Concept . Which of the following is based on the maximal asset values,
which may lead to a highly biased hierarchy a. Cluster analysis b. Segmentation c. Binning d.
Histogram analysis . The can be used to segment numeric data into relatively uniform,
quotnaturalquot intervals. a. rule b. rule c. rule d. rule . hierarchies for numeric attributes can
be constructed automatically based on data distribution analysis
simplicity measure a. Rule strength b. Rule quality c. Rule reliability a. Concept
b. Discretization c. Tree d. Index . techniques can be used to reduce the number of values
for a given continuous attribute, by dividing the range of the attribute into intervals a. Concept
hierarchy b. Discretization c. Treebased d. Index . A algorithm can be applied to partition
data into groups a. Binning b. Histogram c. Clustering d. Entropybased . An
informationbased measure called can be used to recursively partition the values of a numeric
attribute A, resulting in a hierarchical discretization. a. Entropy b. Cluster c. Binning d.
Segmentation . The kinds of knowledge include a. Image analysis b. Query process c.
Association d. Multimedia analysis . Which of the following is a d. Rule length
. A hierarchy is a total or partial order among attributes
. hierarchies can be used to refine or enrich schema b defined hierarchies. When the two
types of hierarchies are combined. a. Schema b. Setgrouping c. Operationderived d.
rulebased . are those that contribute new information or increased performance to the given
pattern set. a. Utility patterns b. Certainty patterns c. Novelty pattern d. Simplicity patterns .
Certainty factor is also known as a. Rule length b. Noice threshold c. Minable view d. Rule
strength . Which of the following primitive specifies the data mining functions to be performed
a. Taskrelevant data b. The kind of knowledge to be mined c. Background knowledge d.
Interestingness measures . may be used to guide the mining process or, after discovery to
evaluate the discovered patterns. a. Taskrelevant data b. The kind of knowledge to be mined
c. Background knowledge d. Interestingness measures in the database schema. a. Schema
b. Setgrouping c. Operationderived d. rulebased . Given a set of taskrelevant
data tuples the confidence of quotA Bquot is defined as a. b. c. d. . hierarchies include the
decoding of information encoded strings information extraction from complex data objects
and data clustering. a. Rulebased b. Operationderived c. Schema d. Set grouping . For
association rules of the form quotA Bquot where A and B are sets of items, support is defined
as a. b. c. d. . Which of the following clause is the taskirrelevant data primitive a. In relevance
to b. Use for warehouse c. Analysis d. Order by
. Mining with the use of , allows additional flexibility for ad hoc rule mining. a. Image patterns
b. Data patterns c. Information patterns d. Meta patterns . Which of the following clause lists
the attributes or dimensions for exploration a. Order by b. group by c. having d. in relevance
to . Which of the following clause uses the meta pattern a. Analyze b. In relevance to c.
Matching d. Use data warehouse . Which of the following clause is used for discrimination a.
Mine characteristics b. Mine discriminant c. Mine association d. Mine comparison . DMQL
expansion is a. Data Modeling Queue Level b. Design Modeling Query language c. Data
Mining Query Language d. Data ampMeta data Query Language . The clause, when used for
characterization, specific aggregate measures, such as count, sum or count . a. Use
database b. Analyze c. Matching d. Use hierarchy
. Which of the following clause specifies the condition by which groups of data are
considered relevant a. Having b. Group by c. Order by d. analyze . The statement is used to
specify the kind of knowledge to be mined. a. Knowledgeminespecification b.
Mineknowledgespecification c. Knowledgespecificationmine d. Specificationmineknowledge .
An example of interestingness measures and threshold values is a. Without support
threshold b. With confidence threshold c. Without Confidence threshold d. With support
threshold . CRISPDM addresses an issue as a. Mapping from datamining problems to
business issues b. Capturing and misunderstanding the data c. Disintegrating datamining
results within the business context d. Deploying and maintaining data mining results . An
Example of a setgrouping hierarchy is a. Define hierarchy agehierarchy for age as customer
on levelyoung, middleaged, serior levelall level level young level level middleaged level
levelsenior
b. Define hierarchy agehierarchy as age for customer on levelyoung, middleaged, serior
levelall level level young level level middleaged level levelsenior c. Define hierarchy
agehierarchy for age on customer as levelyoung, middleaged,serior levelall level level young
level level middleaged level levelsenior d. Define hierarchy agehierarchy on age for customer
as levelyoung, middleaged, serior levelall level level young level level middleaged level
levelsenior . Which of the following data mining language uses SQLlike syntax and serves as
rule generation queries for mining association rules. a. MINE RULE operator b. RULE MINE
operator c. DATA MINE operator d. DWH operator . Which of the following is not a data
mining language a. DMQL b. MSQL c. PSQL d. OLE DB for . System of schema hierarchy is
a. textbfDefine hierarchy locationhierarchy textbfon address textbfas street, city, country b.
textbfDefine hierarchy location
h ierarchy textbfas address textbfon street, city, country c. textbfDefine hierarchy
locationhierarchy textbffrom address textbfto street, city, country d. textbfDefine hierarchy
locationhierarchy textbffor address textbfall street, city, country . The DMQL statement
syntax is a. display as result from b. display result from c. display on result from d. display for
result from . Which of the following is a data mining query language a. PSQL b. QSQL c.
MSQL d. RSQL . is used for efficient implementations of a few essential data mining
primitives. a. No coupling b. Loose coupling c. Tight coupling d. Semi tight coupling . is a
compromise between loose and tight coupling. a. No coupling b. Loose coupling c. Tight
coupling d. Semi tight coupling . Which of the following coupling schema is used to fetch data
from a data repository managed by database systems a. No coupling b. Loose coupling c.
Tight coupling d. Semi tight coupling
. A well designed data mining system should offer with a data warehouse system a. Semi
tight coupling b. No coupling c. Loose coupling d. Normal coupling . Which of the following is
difficult to achieve high scalability and good performance with large data sets a. No coupling
b. Tight coupling c. Semi tight coupling d. Loose coupling . means that a Data mining system
will not utilize any function of a data warehouse system a. Loose coupling b. Semi tight
coupling c. Loose coupling d. No coupling . means that a data mining system is smoothing
integrated coupling database system. a. No coupling b. Loose coupling c. Tight coupling d.
Semi tight coupling . Which of the following provides a concise and succinct summerization
of the given collection of data a. Comparison b. Characterization c. Summerization d.
Aggregation . data mining
describes the data set in a concise and summerative manner and presents interesting
general properties of the data. a. Descriptive b. Predictive c. Active d. Constructive . data
mining analyzes the data in order to construct one or a set of models and attempts to predict
the behavior of new data sets. a. Descriptive b. Predictive c. Active d. Constructive . Attribute
removal is based on the following rule If there is a large set of distinct values for an attribute
of the initial working relation but, a. There is generalization operator on the attribute b. There
is no generalization operand on the attribute c. There is no generalization operator on the
attribute d. There is no aggregation operator on the attribute . Online analysis processing in
data warehouses is a purelycontrolled process a. Machine b. database c. Developer d. User .
Which of the following approach is used to control generalization
d a. Generalized relation threshold a control b. Generalized class threshold control c.
Generalized dimension threshold control d. Generalized query threshold control . Many
current OLAP systems confine dimensions to data a. Numeric b. Non numeric c. Meta d.
Summerized . is a process that abstracts a large set of taskrelevant data in a database from
a relatively low conceptual level to higher conceptual levels. a. Data realization b. Data
characterization c. Data summerization d. Data generalization . The approach can be
considered as a data warehousebased precomputationoriented, material view approach. a.
Objectoriented induction b. Data cube c. Attributeoriented induction d. Data square . Which
of the following approach is a relational database queryoriented, generalizationbased, online
data analysis technique a. Attributeoriented induction b. objectoriented approach c. Data
cube d. Data square
process . performs offline aggregation before an OLAP or Data mining query is submitted for
processing. a. Objectoriented induction b. Data cube c. Attributeoriented induction d. Data
square . The range of tweight is a. b. c. d. . How can the tweight and interestingness
measures in general be used by the data mining system to display only the concept
descriptions that it objectively evaluates as interesting a. By threshold b. By generalization c.
By comparison d. By characterization . The data cube implementation of attributeoriented
induction can be performed by a. Using defined data cube b. Using a predefined data cube c.
Using a generalized data cube d. Using a quantified data cube . A can be represented by a D
data cube. a. Crosstab b. Bar chart c. pie chart d. Flow chart . Step one of the
attributeorientedinduction algorithm is essentially a relational query to collect the task
relevant data into the . a. Prime relation b. Secondary relation c. Working relation d.
Analyzing relation . Which of the following relation collects the statistics of
attributeorientedinduction algorithm a. Working relation b. Prime relation c. Secondary
relation d. Analyzing realation . Descriptions can also be visualized in the form of . a.
Crossralations b. Crosschecks c. Crossboards d. Crosstabs . Step three of
attributeorientedinduction derives the relation. a. Working b. Prime c. Secondary d. Analysing
. The as an interestingness measure that describes the typically of each disjoint in the rule,
or of each tuple in the corresponding generalized relation. a. Quantitative rule b. Quantitative
characteristic rule c. cweight d. tweight . The information gain is obtained by needed to
classify a given sample is a. Is,s.sm mathop Sigma limitsi n /s /s b. Is,s.sm /s /s c. Is,s.sm
mathop Sigma limitsi n /s /s d. Is,s.sm mathop Sigma limitsi n /s /s
a. Expected information entropy b. Entropy Expected information c. Expected information
entropy d. Entropy Expected information . The expected information
. Class comprarison is also called as a. composition b. aggregation c. discrimination d.
characterization . can be used to perform some preliminary relevance analysis on the data by
removing or generalizing attributes having a very large number of distinct values. a.
Objectoriented induction b. Attributeoriented induction c. Batchoriented induction d.
Classoriented induction . Class characterization that includes the analysis of
attribute/dimensions relevance is called . a. Analytical comparison b. Analytical measurement
c. Analytical characterization d. Analytical difference . irrelevant and weakly relevant
attributes using the selected relevance analysis measure. a. Insert b. Update
c. Modify d. Remove . The class is the class to be characterized a. base b. target c.
contrasting d. sub . The class is the set of comparable data that are not in the target class. a.
base b. target c. contrasting d. sub . Generalization is performed on the to the level
controlled by a user or expertspecified dimension threshold, which results in a a. Target
class, Prime target class relation b. Contrasting class, Prime contrasting class relation c.
Target class, Secondary target class relation d. Contrasting class, Secondary contrasting
class relation . Let be a generalized tuple, and be the target class, the dweight is defined as
a. dweight condition / count b. dweight condition / mathop Sigma limitsi m count c. dweight
condition / count d. dweight condition / count . Can class comparison mining be implemented
efficiently using data cube techniques a. yes
b. no c. limited d. difficult . Class discrimination is also called as a. class comparison b. class
hierarchy c. class aggregation d. class concept . The set of relevant data in the database is
collected by query processed and is partitioned respectively into a target class and one or a
set of classes a. discrimination b. contrasting c. comparable d. target . The range for the
dweight is a. b. c. d. . A dweight in the target class indicates that the concept represented by
the generalized tuple is primarily derived from the target class a. Low b. High c. Average d.
Middle . A dweight implies that the concept is primarily derived from the contrasting class a.
Low b. High c. Average d. Middle . A quantitave discriminant rule for the target class of a
given
comparison description is written in the form a. x, target classx comparex d dweight b. x,
contrasting classx conditionx d dweight c. x, contrasting classx comparex d dweight d. x,
target classx conditionx d dweight . In dweight, d stands for a. divide b. dead c. discrimination
d. degree . Inter quartile is defined as a. First quartile Third quartile b. First quartile Third
quartile c. Third quartile First quartile d. Third quartile First quartile . One common rule of
thumb for identifying suspected outliers is to single out values falling at least above the third
quartile or below the first quartile. a. b. c. d. . The most commonly used percentiles other the
median are a. Outliers b. Boxplots c. Quartiles d. Modes . A popularly used visual
representation of a distribution is the a. Boxplot b. Outlier c. Quartile d. Histogram
. Dispersion is also called as a. Mean b. Variance c. Median d. mode . Which of the following
is central tendency measure a. Outliers b. Variance c. Quartiles d. Mode . Which of the
following is a data dispersion measure a. Mean b. Variance c. Mode d. Median . The average
of the largest and smallest values in a data set is called as a. Median b. Mean c. Mid range
d. Mode . The for a set of data is the value that occurs most frequently in the set. a. Median
b. Mean c. Mid range d. Mode . Which of the following is not central tendency measure a.
Variance b. Mean c. Median d. Mode . A is one of the most effective graphical methods or
trend between two quantitative variables. a. qq plot b. scatter plot against the correspondings
quantiles of another.
c. quantile plot d. qqq plot . A is another important exploratory graphic aid that adds a smooth
curve to a scatter plot in order to provide better perception of the pattern of dependence. a.
Loess curve b. Scatter curve c. Bar chat d. Quantile plot . Histograms are also called as
histograms. a. frequency b. variance c. quartile d. outlier . The word loess is short for a. Load
compression b. Local compression c. Load refression d. Local refression . A consists of a set
of rectangles that reflect the counts of the classes present in the given data. a. Quartile plot
b. qq plot c. Histogram d. Loess curves . A is a simple and effective way to have a first look
at an unvariate data distribution. a. qq plot b. scatter plot c. histogram d. quantile plot