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1. Advanced Statistical
Analysis Tool
Statistical Package for the Social
Sciences

2. What is SPSS
 IBM SPSS Modeler is a Data Mining and Text Analytics software application by IBM.
It is a comprehensive predictive analytics platform designed to bring predictive
intelligence to decisions made by individuals, groups, systems or by an enterprise as a
whole.
 It is used to build predictive models and conduct analytics tasks through its user-
friendly Visual Interface by leveraging statistical and data mining algorithms without
programming.
 Originally SPSS stands for Statistical Package for the Social Science but now it
stands for Statistical Product and Service Solutions.

3. Advantages of SPSS
 Analyze and better understand your data and solve complex business.
 Understand large and complex data sets quickly with advanced statistical procedures that
help to ensure high accuracy and quality decision-making.

4. Disadvantages of SPSS
 It cannot be used to analyze a big data set.
 Expensive tool.
 The graph features are not as simple as of Microsoft Excel.

5. Competitors
• R
• SAS
• Python
• Minitab
• Eviews
• Origin

6. Flow of
Course
Module 1
Introduction to Data
Mining
• Introduction to Data
Mining
• SPSS Modeler Interface
Module 2
The Data Mining Process
• CRISP-DM Methodology
Module 3
Modeling Techniques
• Multiple Regression Analysis
• Factor Analysis
• Cluster Analysis
• Discriminant Analysis
• Multidimensional Scaling
• Conjoint Analysis

7. Module 1 Introduction to Data Mining

8. Introduction to Data Mining
Why Data Mining?
 How do we consume the available data, translate it into information and make it
usable?
What is Data Mining?
 Process of discovering insights, patterns and relationships from large amounts of data.
What knowledge can be extracted?
 Descriptive-What has happened and Why did it happen?
 Predictive- What is likely to happen next?

9. Why is Data Mining Important & its
Applications
Data and
Analytics
Create New Business Models
CMO- Attract, grow and
retain customers
COO- Optimize Operations:
Counter Frauds &Threats
CIO/CDO – Maximize Insights,
Ensure Trust, Improve
Economics
CRO- Manage Risks
CFO- Transform management
and financial processes

10. Identify the
Data Mining!
Dividing the customers of a company according to
their gender
Computing Total Sales of a Company
Sorting a student database based on student
identification numbers
Predicting the outcomes of tossing a fair pair dice
Predicting the future stock price of a company
using historical records

11. SPSS Modeler Interface
Stream Canvas
Stream,
Output,
Model
Manager
Data Mining
Lifecycle
Palettes
Nodes

12. Module 2 Data Mining Process

13. Data Mining Process: CRISP - DM
 CRISP-DM stands for Cross Industry Standard Process for Data Mining.
 Business Understanding - What should be accomplished from a business perspective?
 Data Understanding - Acquiring the data needed to accomplish the objective.
 Data Preparation- Selecting and Cleaning the data. May transform/aggregate for analysis.
 Modeling - Selecting technique, building and training the model, accessing accuracy.
 Evaluation - Does the model meet business objectives?
 Deployment - Strategy for deploying the model.

15. Module 3 Modeling Techniques

16. Regression Analysis
 Regression technique is used to assess the strength of a relationship
between one dependent and independent variable(s). It helps in predicting the
value of a dependent variable from one or more independent variables.
 Two types of Regression
 Bivariate Regression: One Dependent Variable, One Independent Variable.
 Multiple Regression: One Dependent Variable, Rest Independent Variable.

17. Regression Examples: Understand
Variables
 Price reduction has any impact on increasing sales.
 Sales has any effect on advertising spend, the number of products introduced,
and the number of sales personnel.
 Female literacy has any impact on increasing the marriage age of the female
child.

18. Bivariate Regression: Introduction
 The simplest of regression analysis is called Bivariate Regression.
 Includes 2 variables.
 One dependent variables that needs to be predicted or explained.
 One independent variable that explains the variance in the dependent variable.
 Regression Analysis is used to predict the value of dependent variable given the values
of independent variables by calculating an equation.
 Example on the next slide

19. Regression Methods
 Enter: All independent variables are entered into the equation in (one step), also
called "forced entry".
 Forward: A variable selection method which begins with a model that contains no
variables (called the Null Model) & then starts adding the most significant variables
one after the other.
• Backward: A variable selection method which begins with a model that contains all
variables under consideration (called the Full Model) then starts removing the least
significant variables one after the other.
 Stepwise: It is a combination of Backward & Forward, it keeps adding and removing
predictors as it builds the model.

20. Bivariate Regression: Example
 A marketing manager wants to predict if the variation in the sales can be explained in
terms of variation in advertising spend
 The equation can be as follows
Sales = Sales with 0 advertising spend + B1 (Advertising Budget) + Error Terms
B1 = Beta Coefficient (Change in sales if there is an advertising budget)
Error Terms: Other factors which can affect variable factors

21. Let’s Understand Practically - Bivariate Regression
 Data Description: A person with 8 years of education is earning $77 per week.
 Problem Statement:
 If an increase in education level has any impact on weekly earnings.
 If there is an increase in education by one unit then how much earnings will be
increased per week?
 Hypothesis: Yes, there is a significant impact.

22. Important Terms in Regression
Regression Coefficient
Regression coefficient is a measure of how strongly each IV (also known as predictor variable)
predicts the DV.
R Values
This is the correlation coefficient. Regression analysis would provide you with two different R
values. A simple R value represents the correlation between the observed values and the
predicted values (based on the regression equation obtained) of the DV. The other R values is
referred to as R Square. R square shows how much variance in the dependent variable is
being explained by the independent variable(s). For example, R Square value of 0.70 would
mean that the IVs in the model can predict 70% of the variance in the DV.

23. Important Terms in Regression
T Value
Any t-value greater than +2 or less than - 2 is acceptable. The higher the t-value, the greater the
confidence (accuracy) we have in the coefficient as a predictor. Low t-values are indications of low
reliability of the predictive power of that coefficient.
F Value
= Explained Variance/ Unexplained Variance
A general rule of thumb that is often used in regression analysis is that if F>2.5 then we can reject the
null hypothesis. We would conclude that there is a least one parameter value that is nonzero.
Df (Degrees of Freedom)
The number of independent variables in our regression model.

24. Important Terms in Regression
Beta Coefficient (Standardized)
The beta coefficient is the degree of change in the outcome variable for every 1-unit of change in the
predictor variable. It ranges from -∞ to +∞.
R
R is Correlation Coefficient that describes the relationship between two independent variables. It
ranges between +1 and - 1 for completely positive and negative correlation respectively.
Beta Coefficient (Unstandardized)
Standardized beta coefficients are expressed in standard deviations whereas unstandardized
coefficients are expressed in raw units.

25. Important Terms in Regression

26. Insights
 If there is 1 unit increase in education the level then there is 0.423
impact on weekly earning.
 R Square value suggests there is 17.9% variability in weekly earning
due to education level.
 Significance: ? (Tell me)

27. Multiple Regression: Introduction
 One dependent variable and more than one independent variable.
 Rest all as same as Bivariate Regression.

28. Let’s Understand Practically - Multiple Regression
 Data Description: Age, Weight & BP of different people have been given.
 Problem Statement:
 If Age & Weight have any impact on the BP of a person.
 If there is an increase in Age or Weight by one unit then how much BP will
be impacted?
 Hypothesis: Yes, there is a significant impact.

29. Insights
 Because the unit of Age & Weight is different so we can only analyze Standardized
Coefficients.
 If there is 1 unit increase in age, then there is 0.346 impact on BP.
 If there is 1 unit increase in weight, then there is 0.481 impact on BP.
 Both predictors are significant.
 R Square value suggests there is 55.8% variability in BP due to Age & Weight.

30. Factor Analysis
 Factor analysis is a technique that is used to reduce a large number of variables
into fewer numbers of factors.
 Factor analysis groups variables with similar characteristics together. Therefore,
with factor analysis, you can produce a small number of factors from a large
number of variables.
 One can use the reduced factors for further analysis.

31. Factor Analysis: How it Works
 When Factor Analysis is applied to the dataset, variables with high correlation
are grouped together.

32. Important Terms in Factor Analysis
• Communality: Communality is the amount of variance a variable shares with
all the other variables being considered. Small values indicate variables that do
not fit well with the factor solution and should possibly be dropped from the
analysis. Normally values Less than .50 are removed.
• Eigen Value: The eigenvalue represents the total variance explained by each
factor. Factors having eigenvalues over one (1) are selected for further study.

33. Let’s Understand Practically - Factor Analysis
 Data Description: Jet Airways Feedback Data
 Sample Size: 20
 Parameters: 10
 Scale: 1 to 7
o 1 Strongly Disagree
o 2 Relatively Disagree
o 3 Disagree
o 4 Neutral
o 5 Agree
o 6 Relatively Agree
o 7 Strongly Agree

34. Parameters
 JA is always on time.
 Seats are comfortable.
 Love the food they provide.
 Air Hostesses are beautiful.
 My boss/ friends also use the same.
 JA has younger air crafts.
 I get advantage of a frequent flyer program.
 Flight timings suit my schedule.
 I feel safe.
 JA matches my lifestyle
and standard.

35. Advantages
 It can be used to identify the hidden dimensions or constraints which may or
may not be apparent from direct analysis.
 It is not extremely difficult to do and at the same time its inexpensive and gives
accurate results.

36. Disadvantages
 The usefulness depends on the researcher’s ability to develop a complete and accurate
set of product attributes. If important attributes are missed the value of procedure is
reduced accordingly.
 Naming of the factors can be difficult multiple attributes can be highly correlated with no
apparent reasons.
 If the observed variables are completely unrelated the factor analysis is unable to
produce meaningful pattern.

37. Cluster Analysis
 Cluster analysis is a powerful data-mining tool that helps organizations to identify
discrete groups of customers, sales transactions, or other types of behaviours and things.
 For example, insurance providers use cluster analysis to detect fraudulent claims, and
banks use it for credit scoring.
 The most common use of cluster analysis is classification.
 Subjects are separated into groups so that each subject is more similar to other subjects in
its group (called a cluster) than to subjects outside the group.
 This technique is used for segmentation.

38. Application - Segmentation
 A company wants to launch a mobile for INR 100000.
 How to decide whom to target for high sales?

39. Similarity between Factor Analysis &
Cluster Analysis
 Cluster analysis and factor analysis are two common statistical
methods that data analysts use to explore and simplify complex data
sets.
 They both aim to group variables or observations based on some
measure of similarity or correlation, but they differ in their purposes
and assumptions.

40. Difference between Factor Analysis &
Cluster Analysis
 In Factor Analysis we look at Correlation but in Cluster Analysis, we
look at the distance.
 In Factor Analysis, we group the statements but in Cluster Analysis, we
group the respondents.

41. Most Important Types of Cluster Analysis
 TwoStep
TwoStep Cluster is a two-step clustering method. The first step makes a single pass through
the data, during which it compresses the raw input data into a manageable set of subclusters.
The second step uses a hierarchical clustering method to progressively merge the subclusters
into larger and larger clusters, without requiring another pass through the data.
 K – Means
K-means clustering is one of the most often used methods and is conducted by creating a
space that has as dimensions as the input variables. K stands for the number of clusters.

42. Let’s Understand Practically - Cluster Analysis
 Let’s take the same example which we took in Factor Analysis

43. Important Terms in Cluster Analysis
 Silhouette Value
The silhouette value is a measure of how similar an object is to its own cluster (cohesion)
compared to other clusters (separation). The silhouette ranges from −1 to +1, where a
high value indicates that the object is well matched to its own cluster and poorly
matched to neighboring clusters.
 The Euclidean Distance
The Euclidean distance process determines the proximity between observations by
drawing a straight line between pairs of observations. Therefore, this process measures
the distance between observations by looking at the length of this line between
observations.

44. Discriminant Analysis
 The primary function of this technique is to assign each observation to a particular group
or category according to the data’s independent characteristics.
 This is similar to Regression Analysis and is used to assess the relationship between
dependent & independent variables.
 In Discriminant Analysis, the dependent variable is categorial or non-metric.
 Dependent variable is called discriminant variable as that discriminates the respondents.

45. Let’s do it Practically - Discriminant Analysis
 There are 3 rounds conducted by a company to hire a candidate. The data for the same has
been taken to declare the result.

46. Important Terms in Discriminant Analysis
 Low value of Wilk’s Lambda reflects high significance. It ranges between 0 to 1.
 The F Test should show a p value less than 0.5.
 Larger the absolute value of standardized coefficients better the predictive power of
variable.
 Canonical Correlation: Should be closer to 1 for a strong correlation.

47. Insights
 Eigen Value is > 1, so it is a good model.
 Canonical Coefficient is near 1 so strong correlation is present.
 Wilks’ Lambda is towards 0 (0.454) which means high significance. High significance
means better discriminating power of the model.
 P is < 0.05 so the discrimination between the groups is highly significant.
 Test1 has highest power of discrimination then Interview then Test2 going by coefficients.
 Best Predictor: Test1, Interview, Test2 going by Structure Matrix.

48. Multidimensional Scaling
 Multidimensional scaling is a visual representation of distances or dissimilarities between
sets of objects.
 Multidimensional Scaling is a family of statistical methods that focus on creating
mappings of items based on distance.
 The input to multidimensional scaling is a distance matrix. The output is typically a two-
dimensional scatterplot, where each of the objects is represented as a point.
 MDS is more impactful because pictures are easier to interpret than numbers & tables.

49. Applications - Multidimensional Scaling
 To identify the image/ position of a product in consumers’ mind.
 The number & nature of dimensions consumers use to perceive a brand.
 To understand market gap so that a company can fit a new product in the market.
 Also called Perceptual Mapping, maps the perceptions of the consumers about the product that a
marketer always needs.
 Market Segmentation
 Assessing advertising effectiveness
 Pricing Analysis
 Channel Decisions

50. Terms Associated - Multidimensional
Scaling
 Stress: This is a lack of fit-measure, higher values of stress indicates the poorer fits. It must be <
0.02 for a great fit.
 RSQ: Squared Correlation: It must be > 0.7 for a great fit.

51. Steps to Conduct MDS
 Formulate the Problem: Specify the purpose of Analysis, Number of Brands (8 to 25) to be
included in the analysis.
 Obtain the Input Data: Refer the next slide.
 Select the MDS Procedure: Perceptions: To Create Spatial Map, Preference: To Decide the
Dimensions.
 Decide on the number of Dimensions: Not more than 3 else it becomes complicated.
 Label the Dimensions and Interpret the Configuration: Will do practically.
 Assess Reliability & Validity: Will do practically.

52. Obtain The Input Data
MDS Input Data
Perceptions
Direct (Similarity
Judgements)
Derived (Attribute
Ratings)
Preferences

53. Example - Multidimensional Scaling
 5 Brands of Mobile Phones – Vivo, Samsung, Mi, Oppo, Huawei
 2 Dimensions - Economic, Features (Look wise)
 Scale: 0 to 10, 0 – Dissimilar, 10 – Similar

54. Conjoint Analysis
 Conjoint analysis is a form of statistical analysis that firms use in market research to
understand how customers value different components or features of their products or
services.
 Conjoint analysis is a statistical analysis and marketing research technique to measure
what consumers value most about your products and services.
 It is a survey-based statistical analysis method.
 For example, a TV manufacturer would want to know if customers value picture or sound
quality more, or if they value low price more than picture quality.

55. Conjoint Analysis – Use Cases
 Buyer decisions
 Customer preferences
 Market sales
 New product pricing
 Selection of the best service or product feature

56. Conjoint Analysis – Let’s Do It Practically
 Juices Example