SPSS Statistics for Data Analysis and Visualization - And visualiation

Klappentext Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualizationSPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code.IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results.* Conduct a more efficient and accurate analysis* Display complex relationships and create better visualizations* Model complex interactions and master predictive analytics* Integrate R and Python with SPSS Statistics for more efficient, more powerful codeThese "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need. Zusammenfassung Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualizationSPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code.IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results.* Conduct a more efficient and accurate analysis* Display complex relationships and create better visualizations* Model complex interactions and master predictive analytics* Integrate R and Python with SPSS Statistics for more efficient, more powerful codeThese "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need. Inhaltsverzeichnis Foreword xxiiiIntroduction xxviiPart I Advanced Statistics 1Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques 3T-Test 7ANCOVA 8MANOVA 13Factor Analysis and Unobserved Variables in SPSS 23AMOS 26Revisiting Factor Analysis and a General ...

List of contents

Foreword xxiiiIntroduction xxviiPart I Advanced Statistics 1Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques 3T-Test 7ANCOVA 8MANOVA 13Factor Analysis and Unobserved Variables in SPSS 23AMOS 26Revisiting Factor Analysis and a General Orientation to AMOS 26The General Model 29Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping 43Monte Carlo Simulation 44Monte Carlo Simulation in IBM SPSS Statistics 44Creating an SPSS Model File 45IBM SPSS Bootstrapping 59Proportions 63Bootstrap Mean 66Bootstrap and Linear Regression 68Chapter 3 Regression with Categorical Outcome Variables 71Regression Approaches in SPSS 72Logistic Regression 73Ordinal Regression Theory 74Assumptions of Ordinal Regression Models 77Ordinal Regression Dialogs 77Ordinal Regression Output 81Categorical Regression Theory 86Assumptions of Categorical Regression Models 87Categorical Regression Dialogs 87Categorical Regression Output 93Chapter 4 Building Hierarchical Linear Models 101Overview of Hierarchical Linear Mixed Models 102A Two-Level Hierarchical Linear Model Example 102Mixed Models...Linear 104Mixed Models...Linear (Output) 113Mixed Models...Generalized Linear 116Mixed Models...Generalized Linear (Output) 120Adjusting Model Structure 126Part II Data Visualization 129Chapter 5 Take Your Data Visualizations to the Next Level 131Graphics Options in SPSS Statistics 132Understanding the Revolutionary Approach in The Grammar of Graphics 136Bar Chart Case Study 138Bubble Chart Case Study 143Chapter 6 The Code Behind SPSS Graphics: Graphics Production Language 147Introducing GPL: Bubble Chart Case Study 147GPL Help 155Bubble Chart Case Study Part Two 156Double Regression Line Case Study 160Arrows Case Study 163MBTI Bubble Chart Case Study 167Chapter 7 Mapping in IBM SPSS Statistics 173Creating Maps with the Graphboard Template Chooser 174Creating a Choropleth of Counts Map 175Creating Other Map Types 179Creating Maps Using Geographical Coordinates 185Chapter 8 Geospatial Analytics 193Geospatial Association Rules 194Case Study: Crime and 311 Calls 194Spatio-Temporal Prediction 207Case Study: Predicting Weekly Shootings 207Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL, and OMS 217Starting with Crosstabs 220Correspondence Analysis 224Multiple Correspondence Analysis 234Crosstabulations 234Applying OMS and GPL to the MCA Perceptual Map 242Chapter 10 Display Complex Relationships with Multidimensional Scaling 249Metric and Nonmetric Multidimensional Scaling 251Nonmetric Scaling of Psychology Sub?]Disciplines 251Multidimenional Scaling Dialog Options 253Multidimensional Scaling Output Interpretation 259Subjective Approach to Dimension Interpretation 264Statistical Approach to Dimension Interpretation 266Part III Predictive Analytics 271Chapter 11 SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics? 275What Is Data Mining? 275What Is IBM SPSS Modeler? 276Can Data Mining Be Done in SPSS Statistics? 278Hypothesis Testing, Type I Error, and Hold-Out Validation 280Significance of the Model and Importance of Each Independent Variable 284The Importance of Finding and Modeling Interactions 284Classic and Important Data Mining Tasks 287Partitioning and Validating 288Feature Selection 291Balancing 294Comparing Results from Multiple Models 295Creating Ensembles 297Scoring New Records 300Chapter 12 IBM SPSS Data Preparation 303Identify Unusual Cases 304Identify Unusual Cases Dialogs 305Identify Unusual Cases Output 311Optimal Binning 315Optimal Binning Dialogs 316Optimal Binning Output 321Chapter 13 Model Complex Interactions with IBM SPSS Neural Networks 325Why "Neural" Nets? 326The Famous Case of Exclusive OR and the Perceptron 328What Is a Hidden Layer and Why Is It Needed? 332Neural Net Results with the XOR Variables 333How the Weights Are Calculated: Error Backpropagation 337Creating a Consistent Partition in SPSS Statistics 340Comparing Regression to Neural Net with the Bank Salary Case Study 341Calculating Mean Absolute Percent Error for Both Models 344Classification with Neural Nets Demonstrated with the Titanic Dataset 349Chapter 14 Powerful and Intuitive: IBM SPSS Decision Trees 355Building a Tree with the CHAID Algorithm 355Review of the CHAID Algorithm 360Adjusting the CHAID Settings 363CRT for Classification 366Understanding Why the CRT Algorithm Produces a Different Tree 368Missing Data 369Changing the CRT Settings 369Comparing the Results of All Four Models 371Alternative Validation Options 373The Scoring Wizard 374Chapter 15 Find Patterns and Make Predictions with K Nearest Neighbors 379Using KNN to Find "Neighbors" 380The Titanic Dataset and KNN Used as a Classifier 381The Trade-Offs between Bias and Variance 386Comparing Our Models: Decision Trees, Neural Nets, and KNN 388Building an Ensemble 391Part IV Syntax, Data Management, and Programmability 393Chapter 16 Write More Effi cient and Elegant Code with SPSS Syntax Techniques 395A Syntax Primer for the Uninitiated 396Making the Connection: Menus and the Grammar of Syntax 401What Is "Inefficient" Code? 403The Case Study 404Customer Dataset 406Fixing the ZIP Codes 407Addressing Case Sensitivity of City Names with UPPER() and LOWER() 409Parsing Strings and the Index Function 410Aggregate and Restructure 410Pasting Variable Names, TO, Recode, and Count 412DO REPEAT Spend Ratios 414Merge 415Final Syntax File 417Chapter 17 Automate Your Analyses with SPSS Syntax and the Output Management System 421Overview of the Output Management System 422Running OMS from Menus 423Contents xxiAutomatically Writing Selected Categories of Output to Different Formats 424Suppressing Output 429Working with OMS data 436Running OMS from Syntax 438Chapter 18 Statistical Extension Commands 441What Is an Extension Command? 441TURF Analysis--Designing Product Bundles 444Large Problems 449Quantile Regression--Predicting Airline Delays 450Comparing Ordinary Least Squares with Quantile Regression Results 455Operational Considerations 459Support Vector Machines--Predicting Loan Default 461Background 461An Example 464Operational Issues 467Computing Cohen's d Measure of Effect Size for a T-Test 468Index 473

Product details

• Authors: K McCormick, Keit McCormick, Keith McCormick, Keith Salcedo Mccormick, Jon Peck, Jon et al Peck, Jesu Salcedo, Jesus Salcedo, Andrew Wheeler
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 15.04.2017
• EAN: 9781119003557
• ISBN: 978-1-119-00355-7
• No. of pages: 456
• Dimensions: 190 mm x 235 mm x 25 mm
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing; Datenbank, Forschungsmethoden, allgemein, Data Warehouse, SPSS, Mathematische und statistische Software, Informationsmanagement / Data Warehouse, Data Warehousing, Database, Informationsverarbeitung (EDV), Statistik / SPSS, Computer Science; Database & Data Warehousing Technologies; Datenanalyse; Datenbanken u. Data Warehousing; Informatik; Statistik