1. Project 5 Data Mining & Structural Equation Modeling
John
Scott
Mark
November 13, 2002
Copyright (c) 2008 by The McGraw-Hill Companies. This material is intended solely for educational use by licensed users of LearningStats. It may not be copied or resold for profit.

2. The Assignment

3. What is Data Mining?
The search for significant patterns or trends in a large databases using statistics
These patterns and trends provide crucial information which helps companies and industries enhance their bottom line and improve products, marketing, sales, and customer service

4. Applications of Data Mining
Fraud detection, credit card scoring and personal profile marketing
Skillful interpretation of data can enhance customer relations, direct marketing, trend analysis, financial market forecasting and international criminal investigations

5. Data Mining Resources Commercial Sites Groups Publications Data Sets
Data-miners.com
Insightful.com
SAS.com
Statistics.com
Groups
Intelligent Database Systems Research Laboratory
Publications
Data Mining and Privacy: A Conflict in the Making
Visualizing Data Mining Models
Data Mining and Customer Relationships
From Data Mining to Data Base Marketing
Data Sets
Cancer data
Criminal Justice data
Silicon Graphics Inc

6. Data Mining Jobs

7. Data Mining Uses Related to Work Related to QMM510
The application of data mining in the automotive industry can best be illustrated through buying trends. Data miners can use the collected information from automobile purchases to determine the next most likely vehicle to be purchased. What the color and amenity tastes will be, even how much they will be willing and able to pay for their next car.
Related to QMM510
Data mining can be used in a setting such as QMM510 (statistics for managers). It requires the use of software, data and the skills acquired from QMM510 to interpret the results. The results can help make better business decisions for an individual or a company.

8. What is Structural Equation Modeling (SEM)?
A very general, very powerful multivariate analysis technique that includes specialized versions of a number of other analysis methods as special cases.
The translation of theory, previous research, design, and common sense into a structural model.
Most structural equation models can be expressed as path diagrams. Consequently even beginners to structural modeling can perform complicated analyses with a minimum of training.

9. SEM Flow Diagram
STEP 1: SPECIFICATION: Statement of the theoretical model in terms of equations or a diagram.
STEP 2: IDENTIFICATION: The model can in theory be estimated with observed data
STEP 3: ESTIMATION: The model's parameters are statistically estimated from data. Multiple regression is one such estimation method, but most often more complicated methods are used.
STEP 4: MODEL FIT: The estimated model parameters are used to predict the correlations or covariances between measured variables and the predicted correlations or covariances are compared to the observed correlations or covariances

10. Applications of SEM
Major applications of structural equation modeling include:
Causal modeling, or path analysis, which hypothesizes causal relationships among variables and tests the causal models with a linear equation system. Causal models can involve either manifest variables, latent variables, or both.
Confirmatory factor analysis, an extension of factor analysis in which specific hypotheses about the structure of the factor loadings and intercorrelations are tested.
Second order factor analysis, a variation of factor analysis in which the correlation matrix of the common factors is itself factor analyzed to provide second order factors.
Regression models, an extension of linear regression analysis in which regression weights may be constrained to be equal to each other, or to specified numerical values.
Covariance structure models, which hypothesize that a covariance matrix has a particular form. For example, you can test the hypothesis that a set of variables all have equal variances with this procedure.
Correlation structure models, which hypothesize that a correlation matrix has a particular form. A classic example is the hypothesis that the correlation matrix has the structure of a circumplex.

11. SEM Resources Online Resources http://www.statsoftine.com

12. Jobs using SEM
Structural Equations Modeling can be used in a broad range of industries. Some of these employment applications are:
Design and analysis in the manufacturing industry
Structural analysis in civil engineering
Design of electronic and optioelectronic devices
Automatic control and simulation

13. SEM Uses Related to Work & MBA Classes
Structural Equations Modeling can be used in a broad range of industries. Some of these employment applications are:
Design and analysis in the manufacturing industry
Structural analysis in civil engineering
Design of electronic and optoelectronic devices
Automatic control and simulation