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1. |
Record Nr. |
UNISALENTO991000426669707536 |
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Autore |
Ciconte, Enzo |
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Titolo |
Storia criminale : la resistibile ascesa di mafia, 'ndrangheta e camorra dall'Ottocento ai giorni nostri / Enzo Ciconte |
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Pubbl/distr/stampa |
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Soveria Mannelli : Rubbettino, [2008] |
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ISBN |
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Descrizione fisica |
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Collana |
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Universale Rubbettino ; 2 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910299253703321 |
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Autore |
Konstantinou Nikolaos |
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Titolo |
Materializing the Web of Linked Data / / by Nikolaos Konstantinou, Dimitrios-Emmanuel Spanos |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (146 p.) |
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Disciplina |
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Soggetti |
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Data mining |
Information organization |
Application software |
Data Mining and Knowledge Discovery |
Information Storage and Retrieval |
Information Systems Applications (incl. Internet) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction: Linked Data and the Semantic Web -- Technical Background -- Deploying Linked Open Data: Methodologies and Software Tools -- Creating Linked Data from Relational Databases -- Generating Linked Data in Real-Time from Sensor Data Streams -- Conclusions. |
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Sommario/riassunto |
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This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from |
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the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general. |
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3. |
Record Nr. |
UNINA9910299777503321 |
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Autore |
Chapman Chris |
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Titolo |
R for Marketing Research and Analytics / / by Chris Chapman, Elea McDonnell Feit |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (459 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Statistics |
Marketing |
R (Computer program language) |
Statistics for Business, Management, Economics, Finance, Insurance |
Statistics and Computing/Statistics Programs |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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includes bibliographical references and index. |
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Nota di contenuto |
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Welcome to R -- The R Language -- Describing Data -- Relationships Between Continuous Variables -- Comparing Groups: Tables and Visualizations -- Comparing Groups: Statistical Tests -- Identifying Drivers of Outcomes: Linear Models -- Reducing Data Complexity -- Additional Linear Modeling Topics -- Confirmatory Factor Analysis and Structural Equation Modeling -- Segmentation: Clustering and Classification -- Association Rules for Market Basket Analysis -- Choice Modeling -- Conclusion -- Appendix: R Versions and Related Software -- Appendix: Scaling up -- Appendix: Packages Used -- Index. |
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Sommario/riassunto |
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This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, |
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presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. |
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