Data Analysis

Presented By:

Shankar Kumud

Kushwaha Sikander

Data Analysis is

Data analysis is a process of:Gathering;Modeling; and Transforming of data.

With the goal of highlighting useful Information; Suggesting conclusions; and Supporting decision making.

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Major Data Analysis Techniques Correlation Analysis; Regression Analysis; Factor Analysis; Cluster Analysis; Correspondence Analysis (Brand

Mapping); Conjoint Analysis; CHAID Analysis; Discriminant /Logistic Regression Analysis; Multidimensional Scaling; and Structural Equation Modeling.


CORRELATION ANALYSIS Correlation analysis, expressed by correlation

coefficients, measures the degree of linear relationship between two variables.

Feature of Correlation coefficient:Between + and – 1;The sign of the correlation coefficient (+, -) defines

the direction of the relationship, +tive or –tive;A positive correlation coefficient means that as the

value of one variable increases, the value of the other variable also increases; as one decreases the other decreases; and

A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.


Cont.. The absolute value of the correlation

coefficient measures the strength of the relationship.

A correlation coefficient of r=0.50 indicates a stronger degree of linear relationship than one of r=0.40.

Correlation coefficient of zero (r=0.0) indicates the absence of a linear relationship.

Correlation coefficients of r=+1.0 and r=-1.0 indicate a perfect linear relationship.


Diagrammatic presentation “r”


R=0.5

R=1

R= -0.5

Regression analysis Regression analysis measures the:

strength of a relationship between a variable (e.g. overall customer satisfaction)

one or more explaining variables (e.g. satisfaction with product quality and price).

Correlation provides a single numeric summary of a relation (called the correlation coefficient), while regression analysis results in a "prediction" equation.

The regression equation describes the relation between the variables. If the relationship is strong (expressed by the Rsquare value), it can be used to predict values of one variable given the other variables have known values.


Cont..

For example: how will the overall satisfaction score change if satisfaction with product quality goes up from 6 to 7.


Factor Analysis Factor analysis aims to

describe a large number of variables or questions by only using a reduced set of underlying variables, called factors.

It explains a pattern of similarity between observed variables. Questions which belong to one factor are highly correlated with each other.

Factor Analysis

Exploratory Confirmatory


Types of Factor Analysis

Use of Factor Analysis Factor analysis is often used in customer satisfaction

studies to identify underlying service dimensions, and in profiling studies to determine core attitudes.

For example, as part of a national survey on political opinions, respondents may answer three separate questions regarding environmental policy, reflecting issues at the local, regional and national level.

Factor analysis can be used to establish whether the three measures do, in fact, measure the same thing.

It is can also prove to be useful when a lengthy questionnaire needs to be shortened, but still retain key questions.

Factor analysis will indicate which questions can be omitted without losing too much information.


CLUSTER ANALYSISCluster analysis is an exploratory tool designed to reveal natural groupings within a large group of observations. Cluster analysis segments the survey sample, i.e. respondents or companies, into a small number of groups.


BRAND MAPPING (CORRESPONDENCE ANALYSIS) Correspondence analysis is a technique

which: Allows rows and columns of a data matrix, E.g. average satisfaction scores for several

products, to be displayed as points in a two dimensional space or map. It reduces a complicated set of data to a graphical display which is immediately and easily interpretable. Brand maps are based on correspondence analysis.

Brand maps are often used to illustrate customers' images of the market by placing products and attributes together on a map. This allows close interpretation of company perceptions with a variety of product and service attributes simultaneously.


CONJOINT ANALYSIS Conjoint analysis is a technique for measuring

respondent preferences about the attributes of a product or service.

It is the ideal tool for new/improved product development.

The conjoint analysis task asks the respondents to make choices in the same fashion as consumers normally do, by trading off features one against the other, either by ranking or choosing one of several product combinations.

E.g. a task could be: do you prefer a "flight that is cramped, costs £250 and has one stop" or a "flight that is spacious, costs £500 and is direct"?


Example of C A

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Importance Of Printer Features, Plus Simulator

Cont..


CHAID ANALYSIS CHAID (Chi Squared Automatic

Interaction Detection) is used to build: a predictive model, based on a

classification system. The analysis subdivides the sample into

a series of subgroups that :○ 1) share similar characteristics towards a

specific response variable and that ○ 2) maximises our ability to predict the

values of the response variable.


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The output is a tree of which the branches are the predictor variables that split the sample in discriminating groups.

DISCRIMINANT/LOGISTIC REGRESSION ANALYSIS Discriminant and logistic

regression analysis are statistical techniques that point out the differences between two or more groups based on several characteristics (most often rating scales when Discriminant analysis, while logistic regression can handle any type of variable)

Is often used : to determine which

customers are likely to buy a company's product

to decide whether a bank should offer a loan to a new company or

to identify patients which may be at high risk for medical problems


Diagrammatic Presentation


MULTIDIMENSIONAL SCALING Multidimensional scaling (MDS) can be

considered to be an alternative to factor analysis.

In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities between the investigated objects. In factor analysis, the similarities between objects (e.g. variables) are expressed in the correlation matrix.

With MDS one may analyse any kind of similarity or dissimilarity matrix, in addition to correlation matrices.


MDS methods are applicable to a wide variety of research designs.


STRUCTURAL EQUATION MODELING Structural Equation Modeling (SEM) is a

very general, very powerful multivariate analysis technique that includes a number of other traditional analysis methods as special cases.

It effectively includes a whole range of standard multivariate analysis methods, such as regression, factor analysis and analysis of variance.

A structural equation model can exist with several regression and factor analysis models, which are estimated simultaneously.


E.g. of a CRM model using survey


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Data Analysis

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