Final

Question 1

Relationship between INCOME (in $) and CONTINENT of birth could be analyzed using an F-test

Answer 1

TRUE

Question 2

Relationship between HEIGHT (in cm) of respondents and CONTINENT of birth could be analyzed using an Chi Squared test

Answer 2

False

Question 3

Relationship between HEIGHT of respondents (in cm) and SEX could be observed using a Scatter Diagram

Answer 3

False

Question 4

Relationship between HEIGHT of respondents (in cm) and WEIGHT (in Kg.) could be observed using a Box Plot

Answer 4

False

Question 5

If we BIN two scale variables HEIGHT of respondents (in cm) and WEIGHT (in Kg) we would get ORDINAL versions that could be analyzed using a crosstab

Answer 5

True

Question 6

t-Test is usually the right option to explore bivariate relationship if we have a scale variable and a categorical variable with more than two categories

Answer 6

False

Question 7

ANOVA test is sometimes followed by a Post - Hoc Test (Bonferroni, LSD

Answer 7

True

Question 8

The Null Hypothesis for an F- test is that the mean of a scale variable is the same across different categories of a categorical variable

Answer 8

True

Question 9

ANOVA is one of the usual inferential tests that complements a SCATTER X/Y graph

Answer 9

False

Question 10

If we get a p-value of 0.04 in a bivariate INDEPENDENCY test, that means that we have evidences of relationship with a 96% of maximum confidence

Answer 10

True

Question 11

CLUSTER is an SUPERVISED CLASSIFICATION method

Answer 11

False

Question 12

Cluster analysis is mainly used to aggregate of CASES, not FIELDS

Answer 12

True

Question 13

Cluster is one of the main technical resources for PREDICTIVE ANALYTICS

Answer 13

False

Question 14

Cluster is one of the main technical resources for PREDICTIVE ANALYTICS

Answer 14

True

Question 15

The evaluation of a cluster solution is NOT MAINLY a technical assessment task

Answer 15

True

Question 16

We normally STANDARDIZE metric and categorical variables to run a cluster analysis

Answer 16

False

Question 17

We normally run a HIERARCHICAL cluster only if the number of cases / individuals is relatively small

Answer 17

True

Question 18

We can use a HIERARCHICAL cluster combining metric and categorical variables

Answer 18

False

Question 19

The AGGLOMERATION schedule is not a piece of interest in a TWO Step Cluster

Answer 19

True

Question 20

The choice of the distance measure in hierarchical clusters depends, basically, on the type of variables (categorical, scale,…)

Answer 20

True

Question 21

One of the advantages of Two-Step is its inherent ability to handle outliers

Answer 21

True

Question 22

Normally, a good cluster solution is shaped with a LARGE number of clustering variables (not less than 15)

Answer 22

False

Question 23

A field/variable can be very relevant to define/ distinguish a SPECIFIC CLUSTER, without being of great importance for the solution as a whole

Answer 23

True

Question 24

The selection of the clustering VARIABLES highly conditions the cluster solution we get

Answer 24

True

Question 25

Factor analysis Is a SUPERVISED analysis technique

Answer 25

false

Question 26

Factor analysis It is normally used when we want to summarize a numerous group of variables into a lower number of factors

Answer 26

True

Question 27

Factor Is commonly used as a technique to reduce the number of cases in a dataset

Answer 27

False

Question 28

We could use a FACTOR analysis to test if different items in a questionnaire are linked with the same latent/underlying concept

Answer 28

True

Question 29

Factor analysis Can be used to create a COMPOSITE INDEX

Answer 29

True

Question 30

A low specificity for most of the inputs variables may suggest that Factor analysis is feasible

Answer 30

True

Question 31

Standard/Classic factor analysis is suitable for metric and categorical variables

Answer 31

False

Question 32

Using PCA extraction method, FACTORS will be always orthogonal if no rotation is applied

Answer 32

True

Question 33

Rotation is used to improve factor interpretation

Answer 33

True

Question 34

Oblique rotation is normally realistic since factors are normally correlated

Answer 34

True

Question 35

Sometimes, we will find of interest to retain factors with eigenvalues lower than ONE

Answer 35

True

Question 36

The factor SCORES will all have zero mean

Answer 36

True

Question 37

Prior to rotation, a variable may exhibit high correlation with more than one factor at the same time

Answer 37

True

Question 38

Correlation between input variables should always be positive if we want to carry a good factor analysis

Answer 38

False

Question 39

In a good factor analysis, we expect to get as few factors as possible accounting for as much variance as possible

Answer 39

True

Question 40

A low level of communality for a variable means that we will need a specific factor for that variable in our analysis

Answer 40

True

Question 41

Given that input variables are used in standardized fashion, the sum of all the eigenvalues of all factors equals the number of variables

Answer 41

True

Question 42

When there exists a common underlying factor for every variable we will get a high eigenvalue for the first factor

Answer 42

True

Question 43

Trees Is a SUPERVISED analysis technique

Answer 43

true

Question 44

Trees It is normally used when we want to balance “causation understanding” Vs prediction

Answer 44

True

Question 45

CHAID uses iterated F-Test to grow the tree

Answer 45

False

Question 46

One of TREES drawbacks is that is a bit hard to interpret because of the technical complexity of output

Answer 46

False

Question 47

It its prone to over-fitting and thus, it requires a careful evaluation

Answer 47

True

Question 48

CHAID is able to use scale variables automatically

Answer 48

True

Question 49

The more we split the nodes, the more we avoid the over – fitting risk

Answer 49

False

Question 50

It is a more flexible than regression when causality relationships are not uniform acroos all our sample

Answer 50

True

Question 51

Trees. It is normally used for scale targets

Answer 51

False

Question 52

We could use the result of a FACTOR analysis as input for a given CLUSTER analysis

Answer 52

True

Question 53

We could use the result of a FACTOR analysis as input for a given REGRESSION analysis

Answer 53

True

Question 54

We could use the result from a CLUSTER analysis as an input in a given TREE Analysis

Answer 54

True

Question 55

We could use the result from a CLUSTER analysis as an input in a given standard FACTOR analysis

Answer 55

False

Question 56

We could use the result from a CLUSTER analysis as an input in a given REGRESSION analysis without any transformation

Answer 56

False

Question 57

We could use STANDARD REGRESSION in order to explain the result of a CLUSTER analysis

Answer 57

False

Question 58

We could use STANDARD REGRESSION in order to explain a given FACTOR score with a set of explanatory variables

Answer 58

True