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The next step is to extract the principal components from this correlation matrix (or covariance matrix), along with the amount of variation explained by each principal component. This is achieved by extracting eigenvectors and eigenvalues from the matrix (Strang, 2006). The eigenvector is a vector of weights or loadings. The eigenvalues represent the amount of variation explained by each factor. The principal components are then sorted according to the amount of variation they account for. Figure 4.13 illustrates a complete principal component analysis for the same 13 variables as used in Fig. 4.12. An eigenvalue is shown for each principal component along with a percentage of the variance explained by each component. The rst principal component (PC1) accounts for 61.9% of the variation in the data, the second principal component (PC2) accounts for 11.2% of the variation in the data, and so on. The weights are also shown for each principal component. For the rst principal component, a loading of 0.00574 is assigned to the age ( years) variable, a loading of 0.345 is assigned to the weight (lbs) variable, and so on. The absolute value of each of the variable s loading values within each principal component represents its relative importance. Additionally, a new variable can be generated from the original variables values and the weights of the principal component. For each original data point, the mean for the variable must initially be subtracted from the value (mean centered) and then multiplied by the variable s weight. These values are then summed to create a new variable for each selected principal component. Figure 4.14 shows a scatterplot representing a derived score from principal component 1 against a derived score for principal component 2.

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effective hydraulic gradient, expressed as rh h hi l 3:9

Figure 2.14 Deformable structures in two and three dimensions, approximated by a piecewise af ne transform.

log P Di di jHidi ; b; 2 ; vjk ; j : Thus the full sample log-likelihood function for the observed measurements to be maximized with respect to (b, 2,vjk,j) is given by: n X Li b; 2 ; vjk ; j L1 b; 2 ; vjk j~0 L2 j L3 b; 2 ; vjk ; j ; a Lrs b; 2 ; vjk ; j

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N Worksheets N Chart sheets N XLM macro sheets (obsolete, but still supported) N Dialog sheets (obsolete, but still supported)

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The shared experiences of life and the support and encouragement of family and friends enabled me to develop this book s ideas and merge them into a coherent whole The support and love of my family provided me with the focus required for an undertaking of this size I am most grateful for my wife, Jill, whose calming presence and time and effort ensured that my sanity and heath were maintained while I worked on this book While it is true that we are a product of our environment and every interaction must have some impact, here I will attempt to recognize particular individuals and organizations that materially affected my thoughts regarding this book The ideas, opinions, and field-tested ideas came from my direct experience with clients in organizations transforming their technology and systems into competitive business facilitators.

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The CRISP-DM project (CRoss Industry Standard Process for Data Mining) has published a data mining process and describes details concerning data mining stages and relationships between the stages. It is available on the web at: http://www.crisp-dm.org/ SEMMA (Sample, Explore, Modify, Model, Assess) describes a series of core tasks for TM model development in the SAS1 Enterprise Miner software and a description can be found at: http://www.sas.com/technologies/analytics/datamining/miner/semma.html

These engagements ranged from the strategic governance of organizations to the tactical definition of metrics within billion-dollar e-commerce environments Specifically I would like to thank Alaska Airlines, Chick-Fil-A, The Coca-Cola Company, CompuCredit, PepsiCo Inc, Synovus, TSYS, The Weather Channel, and their teams of extremely talented executives, managers, and technologists Collaboration with colleagues, partners, and industry professionals provided additional support for and affirmation of the principles discussed in the text I would like to specifically recognize the Association of Certified Fraud Examiners, Deloitte and Touche, the Information Systems Audit and Control Association, the Institute of Internal Auditors, KPMG, and Verisign In every organization with which I have worked, I have found that its strength starts with the passion and talent of its people My greatest pleasure has come from working with these true professionals who seek only to grow and to improve the entire trade.

We mentioned earlier that sometimes you need to test for a condition that is, to see whether it something true or not before proceeding with a script. The use of conditionals like this is quite simple and the conditionals generally use combinations or variations of if, then, and else expressions. These conditionals can use many forms but at the same time must abide by some simple rules.

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