Introduction -- Two basic data mining methods for variable assessment -- CHAID-based data mining for paired-variable assessment -- The importance of straight data : simplicity and desirability for good model-building practice -- Symmetrizing ranked data : a statistical data mining method for improving the predictive power of data -- Principal component analysis : a statistical data mining method for many-variable assessment -- The correlation coefficient : its values range between plus/minus 1, or do they? -- Logistic regression : the workhorse of response modeling -- Ordinary regression : the workhorse of profit modeling -- Variable selection methods in regression : ignorable problem, notable solution -- CHAID for interpreting a logistic regression model -- The importance of the regression coefficient -- The average correlation : a statistical data mining measure for assessment of competing predictive models and the importance of the predictor variables -- CHAID for specifying a model with interaction variables -- Market segmentation classification modeling with logistic regression -- CHAID as a method for filling in missing values -- Identifying your best customers : descriptive, predictive, and look-alike profiling -- Assessment of marketing models -- Bootstrapping in marketing : a new approach for validating models -- Validating the logistic regression model : try bootstrapping -- Visualization of marketing modelsdata mining to uncover innards of a model -- The predictive contribution coefficient : a measure of predictive importance -- Regression modeling involves art, science, and poetry, too -- Genetic and statistic regression models : a comparison --

Data reuse : a powerful data mining effect of the GenIQ model -- A data mining method for moderating outliers instead of discarding them -- Overfitting : old problem, new solution -- The importance of straight data : revisited -- The GenIQ model : its definition and an application -- Finding the best variables for marketing models -- Interpretation of coefficient-free models.