{"product_id":"a-practical-guide-to-data-mining-for-business-and-industry-hardback-9781119977131","title":"A Practical Guide to Data Mining for Business and Industry (Hardback) 9781119977131","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eA Practical Guide to Data Mining for Business and Industry\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAndrea Ahlemeyer-Stubbe (Author), Shirley Coleman (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119977131, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 May 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e328 pages\u003cbr\u003e23.1 x 15.5 x 2.3 cm, 0.522 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e“A Practical Guide to Data Mining for Business and Industrygives practical tools on how information can be extracted from masses of data. The book is very well written, in a conversational tone that makes it enjoyable to read. The authors are excellent communicators. If you are interested in learning about data mining, learning to do a particular task in data mining, looking for a textbook to use in a data mining or analytics course, or have a problem or data analytic task you are working on, this book would be an excellent place to start.”  (\u003ci\u003eMathematical Association of America\u003c\/i\u003e, 23 August 2014)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eData mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. \u003ci\u003ePractical Data Mining for Business\u003c\/i\u003e presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eGlossary of terms xii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Data Mining Concept 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Aims of the Book 3\u003c\/p\u003e \u003cp\u003e1.2 Data Mining Context 5\u003c\/p\u003e \u003cp\u003e1.2.1 Domain Knowledge 6\u003c\/p\u003e \u003cp\u003e1.2.2 Words to Remember 7\u003c\/p\u003e \u003cp\u003e1.2.3 Associated Concepts 7\u003c\/p\u003e \u003cp\u003e1.3 Global Appeal 8\u003c\/p\u003e \u003cp\u003e1.4 Example Datasets Used in This Book 8\u003c\/p\u003e \u003cp\u003e1.5 Recipe Structure 11\u003c\/p\u003e \u003cp\u003e1.6 Further Reading and Resources 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Data Mining Definition 14\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Types of Data Mining Questions 15\u003c\/p\u003e \u003cp\u003e2.1.1 Population and Sample 15\u003c\/p\u003e \u003cp\u003e2.1.2 Data Preparation 16\u003c\/p\u003e \u003cp\u003e2.1.3 Supervised and Unsupervised Methods 16\u003c\/p\u003e \u003cp\u003e2.1.4 Knowledge-Discovery Techniques 18\u003c\/p\u003e \u003cp\u003e2.2 Data Mining Process 19\u003c\/p\u003e \u003cp\u003e2.3 Business Task: Clarification of the Business Question behind the Problem 20\u003c\/p\u003e \u003cp\u003e2.4 Data: Provision and Processing of the Required Data 21\u003c\/p\u003e \u003cp\u003e2.4.1 Fixing the Analysis Period 22\u003c\/p\u003e \u003cp\u003e2.4.2 Basic Unit of Interest 23\u003c\/p\u003e \u003cp\u003e2.4.3 Target Variables 24\u003c\/p\u003e \u003cp\u003e2.4.4 Input Variables\/Explanatory Variables 24\u003c\/p\u003e \u003cp\u003e2.5 Modelling: Analysis of the Data 25\u003c\/p\u003e \u003cp\u003e2.6 Evaluation and Validation during the Analysis Stage 25\u003c\/p\u003e \u003cp\u003e2.7 Application of Data Mining Results and Learning from the Experience 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Data Mining Practicalities 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 All about data 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Some Basics 34\u003c\/p\u003e \u003cp\u003e3.1.1 Data, Information, Knowledge and Wisdom 35\u003c\/p\u003e \u003cp\u003e3.1.2 Sources and Quality of Data 36\u003c\/p\u003e \u003cp\u003e3.1.3 Measurement Level and Types of Data 37\u003c\/p\u003e \u003cp\u003e3.1.4 Measures of Magnitude and Dispersion 39\u003c\/p\u003e \u003cp\u003e3.1.5 Data Distributions 41\u003c\/p\u003e \u003cp\u003e3.2 Data Partition: Random Samples for Training, Testing and Validation 41\u003c\/p\u003e \u003cp\u003e3.3 Types of Business Information Systems 44\u003c\/p\u003e \u003cp\u003e3.3.1 Operational Systems Supporting Business Processes 44\u003c\/p\u003e \u003cp\u003e3.3.2 Analysis-Based Information Systems 45\u003c\/p\u003e \u003cp\u003e3.3.3 Importance of Information 45\u003c\/p\u003e \u003cp\u003e3.4 Data Warehouses 47\u003c\/p\u003e \u003cp\u003e3.4.1 Topic Orientation 47\u003c\/p\u003e \u003cp\u003e3.4.2 Logical Integration and Homogenisation 48\u003c\/p\u003e \u003cp\u003e3.4.3 Reference Period 48\u003c\/p\u003e \u003cp\u003e3.4.4 Low Volatility 48\u003c\/p\u003e \u003cp\u003e3.4.5 Using the Data Warehouse 49\u003c\/p\u003e \u003cp\u003e3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS 50\u003c\/p\u003e \u003cp\u003e3.5.1 Database Management System (DBMS) 51\u003c\/p\u003e \u003cp\u003e3.5.2 Database (DB) 51\u003c\/p\u003e \u003cp\u003e3.5.3 Database Communication Systems (DBCS) 51\u003c\/p\u003e \u003cp\u003e3.6 Data Marts 52\u003c\/p\u003e \u003cp\u003e3.6.1 Regularly Filled Data Marts 53\u003c\/p\u003e \u003cp\u003e3.6.2 Comparison between Data Marts and Data Warehouses 53\u003c\/p\u003e \u003cp\u003e3.7 A Typical Example from the Online Marketing Area 54\u003c\/p\u003e \u003cp\u003e3.8 Unique Data Marts 54\u003c\/p\u003e \u003cp\u003e3.8.1 Permanent Data Marts 54\u003c\/p\u003e \u003cp\u003e3.8.2 Data Marts Resulting from Complex Analysis 56\u003c\/p\u003e \u003cp\u003e3.9 Data Mart: Do’s and Don’ts 58\u003c\/p\u003e \u003cp\u003e3.9.1 Do’s and Don’ts for Processes 58\u003c\/p\u003e \u003cp\u003e3.9.2 Do’s and Don’ts for Handling 58\u003c\/p\u003e \u003cp\u003e3.9.3 Do’s and Don’ts for Coding\/Programming 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Data Preparation 60\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Necessity of Data Preparation 61\u003c\/p\u003e \u003cp\u003e4.2 From Small and Long to Short and Wide 61\u003c\/p\u003e \u003cp\u003e4.3 Transformation of Variables 65\u003c\/p\u003e \u003cp\u003e4.4 Missing Data and Imputation Strategies 66\u003c\/p\u003e \u003cp\u003e4.5 Outliers 69\u003c\/p\u003e \u003cp\u003e4.6 Dealing with the Vagaries of Data 70\u003c\/p\u003e \u003cp\u003e4.6.1 Distributions 70\u003c\/p\u003e \u003cp\u003e4.6.2 Tests for Normality 70\u003c\/p\u003e \u003cp\u003e4.6.3 Data with Totally Different Scales 70\u003c\/p\u003e \u003cp\u003e4.7 Adjusting the Data Distributions 71\u003c\/p\u003e \u003cp\u003e4.7.1 Standardisation and Normalisation 71\u003c\/p\u003e \u003cp\u003e4.7.2 Ranking 71\u003c\/p\u003e \u003cp\u003e4.7.3 Box–Cox Transformation 71\u003c\/p\u003e \u003cp\u003e4.8 Binning 72\u003c\/p\u003e \u003cp\u003e4.8.1 Bucket Method 73\u003c\/p\u003e \u003cp\u003e4.8.2 Analytical Binning for Nominal Variables 73\u003c\/p\u003e \u003cp\u003e4.8.3 Quantiles 73\u003c\/p\u003e \u003cp\u003e4.8.4 Binning in Practice 74\u003c\/p\u003e \u003cp\u003e4.9 Timing Considerations 77\u003c\/p\u003e \u003cp\u003e4.10 Operational Issues 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Analytics 78\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 79\u003c\/p\u003e \u003cp\u003e5.2 Basis of Statistical Tests 80\u003c\/p\u003e \u003cp\u003e5.2.1 Hypothesis Tests and \u003ci\u003eP \u003c\/i\u003eValues 80\u003c\/p\u003e \u003cp\u003e5.2.2 Tolerance Intervals 82\u003c\/p\u003e \u003cp\u003e5.2.3 Standard Errors and Confidence Intervals 83\u003c\/p\u003e \u003cp\u003e5.3 Sampling 83\u003c\/p\u003e \u003cp\u003e5.3.1 Methods 83\u003c\/p\u003e \u003cp\u003e5.3.2 Sample Sizes 84\u003c\/p\u003e \u003cp\u003e5.3.3 Sample Quality and Stability 84\u003c\/p\u003e \u003cp\u003e5.4 Basic Statistics for Pre-analytics 85\u003c\/p\u003e \u003cp\u003e5.4.1 Frequencies 85\u003c\/p\u003e \u003cp\u003e5.4.2 Comparative Tests 88\u003c\/p\u003e \u003cp\u003e5.4.3 Cross Tabulation and Contingency Tables 89\u003c\/p\u003e \u003cp\u003e5.4.4 Correlations 90\u003c\/p\u003e \u003cp\u003e5.4.5 Association Measures for Nominal Variables 91\u003c\/p\u003e \u003cp\u003e5.4.6 Examples of Output from Comparative and Cross Tabulation Tests 92\u003c\/p\u003e \u003cp\u003e5.5 Feature Selection\/Reduction of Variables 96\u003c\/p\u003e \u003cp\u003e5.5.1 Feature Reduction Using Domain Knowledge 96\u003c\/p\u003e \u003cp\u003e5.5.2 Feature Selection Using Chi-Square 97\u003c\/p\u003e \u003cp\u003e5.5.3 Principal Components Analysis and Factor Analysis 97\u003c\/p\u003e \u003cp\u003e5.5.4 Canonical Correlation, PLS and SEM 98\u003c\/p\u003e \u003cp\u003e5.5.5 Decision Trees 98\u003c\/p\u003e \u003cp\u003e5.5.6 Random Forests 98\u003c\/p\u003e \u003cp\u003e5.6 Time Series Analysis 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Methods 102\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Methods Overview 104\u003c\/p\u003e \u003cp\u003e6.2 Supervised Learning 105\u003c\/p\u003e \u003cp\u003e6.2.1 Introduction and Process Steps 105\u003c\/p\u003e \u003cp\u003e6.2.2 Business Task 105\u003c\/p\u003e \u003cp\u003e6.2.3 Provision and Processing of the Required Data 106\u003c\/p\u003e \u003cp\u003e6.2.4 Analysis of the Data 107\u003c\/p\u003e \u003cp\u003e6.2.5 Evaluation and Validation of the Results (during the Analysis) 108\u003c\/p\u003e \u003cp\u003e6.2.6 Application of the Results 108\u003c\/p\u003e \u003cp\u003e6.3 Multiple Linear Regression for use when Target is Continuous 109\u003c\/p\u003e \u003cp\u003e6.3.1 Rationale of Multiple Linear Regression Modelling 109\u003c\/p\u003e \u003cp\u003e6.3.2 Regression Coefficients 110\u003c\/p\u003e \u003cp\u003e6.3.3 Assessment of the Quality of the Model 111\u003c\/p\u003e \u003cp\u003e6.3.4 Example of Linear Regression in Practice 113\u003c\/p\u003e \u003cp\u003e6.4 Regression when the Target is not Continuous 119\u003c\/p\u003e \u003cp\u003e6.4.1 Logistic Regression 119\u003c\/p\u003e \u003cp\u003e6.4.2 Example of Logistic Regression in Practice 121\u003c\/p\u003e \u003cp\u003e6.4.3 Discriminant Analysis 126\u003c\/p\u003e \u003cp\u003e6.4.4 Log-Linear Models and Poisson Regression 128\u003c\/p\u003e \u003cp\u003e6.5 Decision Trees 129\u003c\/p\u003e \u003cp\u003e6.5.1 Overview 129\u003c\/p\u003e \u003cp\u003e6.5.2 Selection Procedures of the Relevant Input Variables 134\u003c\/p\u003e \u003cp\u003e6.5.3 Splitting Criteria 134\u003c\/p\u003e \u003cp\u003e6.5.4 Number of Splits (Branches of the Tree) 135\u003c\/p\u003e \u003cp\u003e6.5.5 Symmetry\/Asymmetry 135\u003c\/p\u003e \u003cp\u003e6.5.6 Pruning 135\u003c\/p\u003e \u003cp\u003e6.6 Neural Networks 137\u003c\/p\u003e \u003cp\u003e6.7 Which Method Produces the Best Model? A Comparison of Regression, Decision Trees and Neural Networks 141\u003c\/p\u003e \u003cp\u003e6.8 Unsupervised Learning 142\u003c\/p\u003e \u003cp\u003e6.8.1 Introduction and Process Steps 142\u003c\/p\u003e \u003cp\u003e6.8.2 Business Task 143\u003c\/p\u003e \u003cp\u003e6.8.3 Provision and Processing of the Required Data 143\u003c\/p\u003e \u003cp\u003e6.8.4 Analysis of the Data 145\u003c\/p\u003e \u003cp\u003e6.8.5 Evaluation and Validation of the Results (during the Analysis) 147\u003c\/p\u003e \u003cp\u003e6.8.6 Application of the Results 148\u003c\/p\u003e \u003cp\u003e6.9 Cluster Analysis 148\u003c\/p\u003e \u003cp\u003e6.9.1 Introduction 148\u003c\/p\u003e \u003cp\u003e6.9.2 Hierarchical Cluster Analysis 149\u003c\/p\u003e \u003cp\u003e6.9.3 K-Means Method of Cluster Analysis 150\u003c\/p\u003e \u003cp\u003e6.9.4 Example of Cluster Analysis in Practice 151\u003c\/p\u003e \u003cp\u003e6.10 Kohonen Networks and Self-Organising Maps 151\u003c\/p\u003e \u003cp\u003e6.10.1 Description 151\u003c\/p\u003e \u003cp\u003e6.10.2 Example of SOMs in Practice 152\u003c\/p\u003e \u003cp\u003e6.11 Group Purchase Methods: Association and Sequence Analysis 155\u003c\/p\u003e \u003cp\u003e6.11.1 Introduction 155\u003c\/p\u003e \u003cp\u003e6.11.2 Analysis of the Data 157\u003c\/p\u003e \u003cp\u003e6.11.3 Group Purchase Methods 158\u003c\/p\u003e \u003cp\u003e6.11.4 Examples of Group Purchase Methods in Practice 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Validation and Application 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Methods for Validation 161\u003c\/p\u003e \u003cp\u003e7.2 Lift and Gain Charts 162\u003c\/p\u003e \u003cp\u003e7.3 Model Stability 164\u003c\/p\u003e \u003cp\u003e7.4 Sensitivity Analysis 167\u003c\/p\u003e \u003cp\u003e7.5 Threshold Analytics and Confusion Matrix 169\u003c\/p\u003e \u003cp\u003e7.6 ROC Curves 170\u003c\/p\u003e \u003cp\u003e7.7 Cross-Validation and Robustness 171\u003c\/p\u003e \u003cp\u003e7.8 Model Complexity 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Data Mining in Action 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Marketing: Prediction 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Recipe 1: Response Optimisation: to Find and Address the Right Number of Customers 176\u003c\/p\u003e \u003cp\u003e8.2 Recipe 2: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Highest Affinity to an Offer 186\u003c\/p\u003e \u003cp\u003e8.3 Recipe 3: To Find the Right Number of Customers to Ignore 187\u003c\/p\u003e \u003cp\u003e8.4 Recipe 4: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Lowest Affinity to an Offer 190\u003c\/p\u003e \u003cp\u003e8.5 Recipe 5: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Highest Affinity to Buy 191\u003c\/p\u003e \u003cp\u003e8.6 Recipe 6: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Lowest Affinity to Buy 192\u003c\/p\u003e \u003cp\u003e8.7 Recipe 7: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Highest Affinity to a Single Purchase 193\u003c\/p\u003e \u003cp\u003e8.8 Recipe 8: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Highest Affinity to Sign a Long-Term Contract in Communication Areas 194\u003c\/p\u003e \u003cp\u003e8.9 Recipe 9: To Find the \u003ci\u003ex\u003c\/i\u003e% of Customers with the Highest Affinity to Sign a Long-Term Contract in Insurance Areas 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Intra-Customer Analysis 198\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Recipe 10: To Find the Optimal Amount of Single Communication to Activate One Customer 199\u003c\/p\u003e \u003cp\u003e9.2 Recipe 11: To Find the Optimal Communication Mix to Activate One Customer 200\u003c\/p\u003e \u003cp\u003e9.3 Recipe 12: To Find and Describe Homogeneous Groups of Products 206\u003c\/p\u003e \u003cp\u003e9.4 Recipe 13: To Find and Describe Groups of Customers with Homogeneous Usage 210\u003c\/p\u003e \u003cp\u003e9.5 Recipe 14: To Predict the Order Size of Single Products or Product Groups 216\u003c\/p\u003e \u003cp\u003e9.6 Recipe 15: Product Set Combination 217\u003c\/p\u003e \u003cp\u003e9.7 Recipe 16: To Predict the Future Customer Lifetime Value of a Customer 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Learning from a Small Testing Sample and Prediction 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Recipe 17: To Predict Demographic Signs (Like Sex, Age, Education and Income) 225\u003c\/p\u003e \u003cp\u003e10.2 Recipe 18: To Predict the Potential Customers of a Brand New Product or Service in Your Databases 236\u003c\/p\u003e \u003cp\u003e10.3 Recipe 19: To Understand Operational Features and General Business Forecasting 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Miscellaneous 244\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Recipe 20: To Find Customers Who Will Potentially Churn 244\u003c\/p\u003e \u003cp\u003e11.2 Recipe 21: Indirect Churn Based on a Discontinued Contract 249\u003c\/p\u003e \u003cp\u003e11.3 Recipe 22: Social Media Target Group Descriptions 250\u003c\/p\u003e \u003cp\u003e11.4 Recipe 23: Web Monitoring 254\u003c\/p\u003e \u003cp\u003e11.5 Recipe 24: To Predict Who is Likely to Click on a Special Banner 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Software and Tools: A Quick Guide 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 List of Requirements When Choosing a Data Mining Tool 261\u003c\/p\u003e \u003cp\u003e12.2 Introduction to the Idea of Fully Automated Modelling (FAM) 265\u003c\/p\u003e \u003cp\u003e12.2.1 Predictive Behavioural Targeting 265\u003c\/p\u003e \u003cp\u003e12.2.2 Fully Automatic Predictive Targeting and Modelling Real-Time Online Behaviour 266\u003c\/p\u003e \u003cp\u003e12.3 FAM Function 266\u003c\/p\u003e \u003cp\u003e12.4 FAM Architecture 267\u003c\/p\u003e \u003cp\u003e12.5 FAM Data Flows and Databases 268\u003c\/p\u003e \u003cp\u003e12.6 FAM Modelling Aspects 269\u003c\/p\u003e \u003cp\u003e12.7 FAM Challenges and Critical Success Factors 270\u003c\/p\u003e \u003cp\u003e12.8 FAM Summary 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Overviews 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 To Make Use of Official Statistics 272\u003c\/p\u003e \u003cp\u003e13.2 How to Use Simple Maths to Make an Impression 272\u003c\/p\u003e \u003cp\u003e13.2.1 Approximations 272\u003c\/p\u003e \u003cp\u003e13.2.2 Absolute and Relative Values 273\u003c\/p\u003e \u003cp\u003e13.2.3 % Change 273\u003c\/p\u003e \u003cp\u003e13.2.4 Values in Context 273\u003c\/p\u003e \u003cp\u003e13.2.5 Confidence Intervals 274\u003c\/p\u003e \u003cp\u003e13.2.6 Rounding 274\u003c\/p\u003e \u003cp\u003e13.2.7 Tables 274\u003c\/p\u003e \u003cp\u003e13.2.8 Figures 274\u003c\/p\u003e \u003cp\u003e13.3 Differences between Statistical Analysis and Data Mining 275\u003c\/p\u003e \u003cp\u003e13.3.1 Assumptions 275\u003c\/p\u003e \u003cp\u003e13.3.2 Values Missing Because ‘Nothing Happened’ 275\u003c\/p\u003e \u003cp\u003e13.3.3 Sample Sizes 276\u003c\/p\u003e \u003cp\u003e13.3.4 Goodness-of-Fit Tests 276\u003c\/p\u003e \u003cp\u003e13.3.5 Model Complexity 277\u003c\/p\u003e \u003cp\u003e13.4 How to Use Data Mining in Different Industries 277\u003c\/p\u003e \u003cp\u003e13.5 Future Views 283\u003c\/p\u003e \u003cp\u003eBibliography 285\u003c\/p\u003e \u003cp\u003eIndex 296\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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