- Description
- xxxiii, 655 pages : illustrations ; 26 cm.
- Additional Authors
- Ratner, Bruce.
- Notes
- Contents: Preface -- Preface to second edition -- Acknowledgments -- About the author -- Introduction -- Science dealing with data: statistic and data science -- 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 -- Market share estimation : data mining for an exception case -- The correlation coefficient : its values range between plus/minus 1, or do they? -- Logistic regression : the workhorse of response modeling -- Predicting share of wallet without survey data -- 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 -- Market segmentation based on time-series data using latent class analysis -- Market segmentation: an easy way to understand the segments -- Chaid as a method for filling in missing values -- Model building with big complete and incomplete data -- Art, science, numbers, and poetry -- Identifying your best customers: descriptive, predictive, and look-alike profiling -- Assessment of marketing models -- Decile analysis: perspective and performance -- Net T-C lift model : assessing the net effects of test and control campaigns -- Bootstrapping in marketing -- Validating the logistic regression model : try bootstrapping -- Visualization of marketing models data 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 -- Text mining : primer, illustration, and txtdm software -- Some of my favorite statistical subroutines -- Index.Includes bibliographical references and index.Revised edition of the author's Statistical and machine-learning data mining, c2003.