SPSS Statistics for Data Analysis and Visualization

Creating Maps Using Geographical Coordinates 185 Chapter 8 Geospatial Analytics 193 Geospatial Association Rules 194 Case Study: Crime and 311 Calls 194 Spatio-Temporal Prediction 207 Case Study: Predicting Weekly Shootings 207 Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL, and OMS 217 Starting with Crosstabs 220 Correspondence Analysis 224 Multiple Correspondence Analysis 234 Crosstabulations 234 Applying OMS and GPL to the MCA Perceptual Map 242 Chapter 10 Display Complex Relationships with Multidimensional Scaling 249 Metric and Nonmetric Multidimensional Scaling 251 Nonmetric Scaling of Psychology Sub?]Disciplines 251 Multidimenional Scaling Dialog Options 253 Multidimensional Scaling Output Interpretation 259 Subjective Approach to Dimension Interpretation 264 Statistical Approach to Dimension Interpretation 266 Part III Predictive Analytics 271 Chapter 11 SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics? 275 What Is Data Mining? 275 What Is IBM SPSS Modeler? 276 Can Data Mining Be Done in SPSS Statistics? 278 Hypothesis Testing, Type I Error, and Hold-Out Validation 280 Significance of the Model and Importance of Each Independent Variable 284 The Importance of Finding and Modeling Interactions 284 Classic and Important Data Mining Tasks 287 Partitioning and Validating 288 Feature Selection 291 Balancing 294 Comparing Results from Multiple Models 295 Creating Ensembles 297 Scoring New Records 300 Chapter 12 IBM SPSS Data Preparation 303 Identify Unusual Cases 304 Identify Unusual Cases Dialogs 305 Identify Unusual Cases Output 311 Optimal Binning 315 Optimal Binning Dialogs 316 Optimal Binning Output 321 Chapter 13 Model Complex Interactions with IBM SPSS Neural Networks 325 Why "Neural" Nets? 326 The Famous Case of Exclusive OR and the Perceptron 328 What Is a Hidden Layer and Why Is It Needed? 332 Neural Net Results with the XOR Variables 333 How the Weights Are Calculated: Error Backpropagation 337 Creating a Consistent Partition in SPSS Statistics 340 Comparing Regression to Neural Net with the Bank Salary Case Study 341 Calculating Mean Absolute Percent Error for Both Models 344 Classification with Neural Nets Demonstrated with the Titanic Dataset 349 Chapter 14 Powerful and Intuitive: IBM SPSS Decision Trees 355 Building a Tree with the CHAID Algorithm 355 Review of the CHAID Algorithm 360 Adjusting the CHAID Settings 363 CRT for Classification 366 Understanding Why the CRT Algorithm Produces a Different Tree 368 Missing Data 369 Changing the CRT Settings 369 Comparing the Results of All Four Models 371 Alternative Validation Options 373 The Scoring Wizard 374 Chapter 15 Find Patterns and Make Predictions with K Nearest Neighbors 379 Using KNN to Find "Neighbors" 380 The Titanic Dataset and KNN Used as a Classifier 381 The Trade-Offs between Bias and Variance 386 Comparing Our Models: Decision Trees, Neural Nets, and KNN 388 Building an Ensemble 391 Part IV Syntax, Data Management, and Programmability 393 Chapter 16 Write More Effi cient and Elegant Code with SPSS Syntax Techniques 395 A Syntax Primer for the Uninitiated 396 Making the Connection: Menus and the Grammar of Syntax 401 What Is "Inefficient" Code? 403 The Case Study 404 Customer Dataset 406 Fixing the ZIP Codes 407 Addressing Case Sensitivity of City Names with UPPER() and LOWER() 409 Parsing Strings and the Index Function 410 Aggregate and Restructure 410 Pasting Variable Names, TO, Recode, and Count 412 DO REPEAT Spend Ratios 414 Merge 415 Final Syntax File 417 Chapter 17 Automate Your Analyses with SPSS Syntax and the Output Management System 421 Overview of the Output Management System 422 Running OMS from Menus 423 Contents xxi Automatically Writing Selected Categories of Output to Different Formats 424 Suppressing Output 429 Working with OMS data 436 Running OMS from Syntax 438 Chapter 18 Statistical Extension Commands 441 What Is an Extension Command? 441 TURF Analysis--Designing Product Bundles 444 Large Problems 449 Quantile Regression--Predicting Airline Delays 450 Comparing Ordinary Least Squares with Quantile Regression Results 455 Operational Considerations 459 Support Vector Machines--Predicting Loan Default 461 Background 461 An Example 464 Operational Issues 467 Computing Cohen's d Measure of Effect Size for a T-Test 468 Index 473